Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|1.7|Washington, DC|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|D.C. Department of Health, Center for Policy, Planning and Evaluation, Vital Statistics mortality files 2010-2014 (Final as of October 27, 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.2|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.5|3.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.3|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.6|3.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.0|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||2.2|3.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|4.4|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.4|4.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.2|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.4|6.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.4|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.3|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||9.8|12.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.8|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.1|14.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|6.8|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.8|7.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.4|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.3|0.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|1.0|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|2.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|1.8|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.7|1.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|2.0|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|5.1|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|10.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.9|14.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|1.6|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.5|1.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.1|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||1.3|3.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.5|6.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.9|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|2.0|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|6.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|8.3|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.3|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.4|4.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.5|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||2.9|6.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|6.0|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.9|6.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.9|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.6|9.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|8.0|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|17.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.9|22.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|17.9|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||15.3|20.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.8|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||1.8|4.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.3|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.2|3.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.4|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.5|5.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.5|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|5.7|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|10.2|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.8|14.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|10.2|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.2|12.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|2.6|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.6|4.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|2.8|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|4.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|3.2|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||2.1|4.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|5.6|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.4|5.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|5.9|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.8|7.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|6.3|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|7.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|12.4|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||10.2|14.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.2|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.4|18.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.2|Washington, DC|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|D.C. Department of Health, Center for Policy, Planning and Evaluation, Vital Statistics mortality files 2010-2014 (Final as of October 27, 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.5|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|3.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.5|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.6|4.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.5|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.9|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.2|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.5|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.5|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.4|4.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.1|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.7|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.9|6.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.5|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|12.1|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.4|15.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|12.2|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||10.7|13.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||American Indian alone. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|6.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.7|7.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|50.2|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.0|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.4|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.3|0.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|2.5|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.3|4.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|1.9|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|2.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|3.1|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|3.4|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|4.5|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|4.8|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|9.1|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.9|13.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|11.8|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|0.9|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|1.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.6|1.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.2|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.3|3.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.9|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.2|5.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.1|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|8.9|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|0.0|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|3.1|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|7.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|8.1|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.1|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.9|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.7|6.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|5.8|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.1|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.1|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.0|6.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|7.1|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.9|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.9|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.4|10.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.3|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|10.7|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.5|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||13.4|22.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|20.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||17.4|23.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.8|Phoenix, AZ|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.1|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.5|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.4|3.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.6|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.6|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.8|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.9|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.9|6.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.5|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|10.0|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.0|11.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|10.5|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.2|15.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Value is for all of Tarrant County||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|3.8|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.6|5.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.2|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.7|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.8|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.3|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.6|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.4|5.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.0|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.3|7.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.4|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.1|7.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.6|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|12.5|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.6|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.7|18.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.4|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||12.1|16.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|1.5|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.2|1.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.0|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.4|2.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.0|Washington, DC|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|D.C. Department of Health, Center for Policy, Planning and Evaluation, Vital Statistics mortality files 2010-2014 (Final as of October 27, 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.2|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.6|3.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.3|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.6|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.2|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.3|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.6|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.2|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.2|4.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.3|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.5|6.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.4|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.5|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Vital Statistics death data for Denver county||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.7|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.6|12.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.8|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.9|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.4|13.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||9.8|12.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|6.6|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||American Indian alone. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|7.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.6|8.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|28.2|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|0.4|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.4|0.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.1|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.3|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.6|5.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|0.0|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|0.8|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1.2|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1.9|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.0|2.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1.9|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|2.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|3.0|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|4.4|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.6|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.2|15.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|15.0|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|0.8|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|1.0|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.7|1.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|1.2|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|1.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.6|1.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.2|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.4|3.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.6|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.9|5.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|10.1|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|0.0|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.5|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|6.1|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.1|12.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.6|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.9|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.3|3.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.6|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.5|5.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|4.1|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.6|5.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|6.0|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|6.6|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.7|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.8|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.5|9.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|8.2|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|8.8|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.6|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.3|13.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.1|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.1|21.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|17.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||14.8|19.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|0.8|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.5|1.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.9|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.4|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.8|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.3|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.2|3.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.8|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.9|4.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Vital Statistics death data for Denver county||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.8|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.9|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.9|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.6|10.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|9.7|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.5|14.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.7|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||9.6|13.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|2.2|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|2.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|2.5|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.5|3.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|2.6|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|2.7|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|3.1|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.1|4.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|3.8|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|4.3|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.1|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.0|5.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.2|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.2|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.8|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.6|8.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|7.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Vital Statistics death data for Denver county||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.8|12.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.1|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.4|16.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.3|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||9.1|16.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|13.9|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|1.1|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.7|1.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|1.8|Washington, DC|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|D.C. Department of Health, Center for Policy, Planning and Evaluation, Vital Statistics mortality files 2010-2014 (Final as of October 27, 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|1.9|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.6|2.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.1|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.4|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.5|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|3.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.6|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.2|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.2|4.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.4|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.6|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.9|5.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.9|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.3|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Vital Statistics death data for Denver county||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.0|10.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.3|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.3|11.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.6|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.2|12.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.1|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|5.8|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||American Indian alone. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|6.6|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.6|7.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|69.4|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.0|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.4|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.1|0.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.4|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.3|0.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|3.0|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.6|5.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|1.4|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.7|2.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|1.6|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|1.6|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|2.1|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.9|2.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|3.3|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|3.8|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|5.5|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|7.0|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.4|10.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|10.0|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|0.3|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.4|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.0|1.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.6|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.9|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.7|2.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.0|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.2|3.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.0|5.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|10.0|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|0.0|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|3.3|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|7.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.5|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.7|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.0|4.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.3|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.6|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.7|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.6|5.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|6.7|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.4|8.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.2|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.3|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|11.2|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.5|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.1|16.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.9|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||9.4|17.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.6|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||11.4|15.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|0.9|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.6|1.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.4|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.7|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.1|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.2|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.4|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.4|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.3|3.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.8|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.9|4.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.3|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Vital Statistics death data for Denver county||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.6|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.9|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|7.0|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.7|10.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|7.1|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.4|10.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|9.1|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.2|10.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|2.7|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|2.9|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.4|3.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|3.2|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|3.3|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.2|4.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|4.0|Houston, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Death data source: Texas Department of State Health Services; Center for Health Statistics. Population data source for Age-Adjusted Rates: Texas Demographic Center; 2011, 2012, and 2013 Texas Population Estimates for Harris County http://osd.texas.gov/Data/TPEPP/Estimates/ Population data source for crude rates:2010 Cenus;Race/Ethnicity calculations provided through Vital Pro|Age-Adjusted Rates per 100,000 using the 2000 US standard population. Crude Rates per 100,000 using 2010 census; Race/Ethnicity calculations through VitalPro. Report age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4.|Data for Harris County, TX. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.1|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.0|5.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.3|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.3|6.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.8|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.3|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.8|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|8.2|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Vital Statistics death data for Denver county||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|9.4|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.6|11.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.6|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.5|15.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|12.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.4|16.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.5|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|1.8|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.5|2.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.1|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.5|2.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.2|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.0|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.2|3.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.4|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.8|4.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.7|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.3|Washington, DC|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|D.C. Department of Health, Center for Policy, Planning and Evaluation, Vital Statistics mortality files 2010-2014 (Final as of October 27, 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.0|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.9|5.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.4|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.4|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.1|9.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.9|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.0|11.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.2|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.9|12.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.6|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.9|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|0.0|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||American Indian alone. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|7.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.6|8.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|28.2|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.0|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.3|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|0.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.5|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.4|0.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|1.1|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|2.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|1.5|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.7|2.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|1.5|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|2.7|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.5|2.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|3.8|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|4.8|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|5.2|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|5.4|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.9|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|9.2|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|10.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.1|15.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|0.8|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.6|1.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|1.2|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|1.6|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|1.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.6|3.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|1.9|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|2.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.9|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.4|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|10.2|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|0.0|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|13.1|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|0.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|1.1|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|3.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.7|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.6|5.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|4.1|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.4|4.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|5.3|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.3|6.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.3|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.4|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.8|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.7|6.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.4|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.9|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.4|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.3|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.1|13.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.9|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||10.7|15.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|13.1|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.6|17.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.1|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|0.9|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.7|1.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.7|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.1|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.3|3.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.2|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.4|4.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.7|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.9|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.8|4.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.6|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|5.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.1|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.2|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.0|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.7|10.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|8.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||5.2|11.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|8.7|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.9|10.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|10.2|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|2.1|Fort Worth (Tarrant County), TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|National Center for Health Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.3|3.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|2.6|Los Angeles, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality Data: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 2012-2014. Standard Population: 2000 U.S. Standard Population. Population: National Center for Health Statistics. Vintage 2012-2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014). Prepared under a collaborative arrangement with the U.S. Census Bureau.|The target population consists of deaths with X40-X44 as an underlying cause and one or more of the following in any multiple cause of death field: T40.2, T40.3 and T40.4.  Computing age-adjusted mortality rate and confidence interval (CI) are performed using the BCHC requested metholodgy except that 2012-2014 bridged-race postcensal population esitmates are applied as denominator. Suppression rule is implemented to ensure that rate and CI of any cell with fewer than 5 deaths are excluded from the table.|These data are county level. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.2|3.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|2.7|Miami (Miami-Dade County), FL|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|3.6|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.8|4.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|3.8|Phoenix, AZ|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Maricopa County Vital Statistics (Death Records)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|3.9|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.7|5.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|6.1|U.S. Total, U.S. Total|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC WONDER||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.9|6.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|7.7|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|7.9|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||6.2|9.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.1|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.3|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.2|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.9|14.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|11.6|Portland (Multnomah County), OR|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|CDC Wonder: X40-X44 AND one or more of the following: T40.0-T40.4||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||8.5|15.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|13.7|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|13.7|San Antonio, TX|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND  one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population. Rates per 100,000. Age adjust rates using the 2000 US standard population. Data from 2012, 2013, and 2014 was used|Texas Death Certificates, Preliminary data subject to change. |Report age-adjusted opioid-related mortality rate using ICD-10 codes: 40-X44; AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. 2011, 2012, 2013 and 2014 per 100,000 population, age adjusted 11 age groups, 2000 US Standard Million|Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.3|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||1.6|3.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.9|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.1|5.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|4.2|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.5|4.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|5.2|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.6|7.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.9|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|7.3|Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.6|9.3
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|7.4|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.7|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.9|17.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|15.4|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|44.1|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|1.6|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|3.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|3.2|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|3.4|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.9|5.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|3.9|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|4.9|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|8.2|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|11.7|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.7|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.8|4.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.8|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.4|4.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|8.4|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|13.0|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|4.8|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|0.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|1.2|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|3.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|14.9|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.8|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||2.3|5.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|5.8|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.6|6.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|7.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|7.8|Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.9|10.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|8.5|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|8.9|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.1|10.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|20.2|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.7|25.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|23.7|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.9|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.2|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.1|4.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.8|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.0|4.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.0|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.5|7.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.5|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|7.4|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|8.9|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|9.4|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.3|13.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|2.5|San Antonio, TX|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Bexar County level data|||1.6|3.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.5|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.5|5.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.7|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.4|6.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|6.8|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|7.5|Las Vegas (Clark County), NV|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Nevada Vital Records - Clark County Deaths||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.8|9.2
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|7.5|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|8.2|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|10.8|Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder X40-X44 AND one or more of the following T40.0-T40.4||This indicator is not exclusive of AI/AN drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), AI/AN and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||7.9|14.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||13.5|23.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|22.4|Boston, MA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|2.8|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.1|3.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|4.0|Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.7|5.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|4.1|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.5|4.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|5.6|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|7.6|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|11.0|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|22.7|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.8|26.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|131.1|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|0.0|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|0.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|2.0|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|5.9|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|17.3|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|19.0|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||13.2|26.6
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|0.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|1.9|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.1|2.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|4.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|9.9|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|0.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|6.1|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|11.4|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|0.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|4.0|Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.7|5.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|6.2|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||5.0|7.4
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|7.3|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|7.7|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|8.4|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|27.9|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.6|34.1
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.4|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||1.5|3.7
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.9|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.1|3.8
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|5.2|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|6.9|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|8.7|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|14.7|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.7|19.9
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||0.0|0.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|3.1|Oakland (Alameda County), CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder|Age-adjusted rate of opioid-related mortality rate using ICD-10 underlying cause of death codes: X40-X44, X60-64, X85 and Y10-14 AND one or more of the following in any multiple cause of death field: T40.0, T40.1, T40.2, T40.3, T40.4.|Data is for Alameda County. This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||2.1|4.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.2|Portland (Multnomah County), OR|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|CDC Wonder||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||3.2|8.0
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.3|San Diego County, CA|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Jan 17, 2018 7:06:49 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.|||4.3|6.5
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.9|Kansas City, MO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|8.1|Denver, CO|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Mortality data from the Colorado Department of Public Health and Environment||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|13.1|Minneapolis, MN|Age-adjusted rate of opioid-related mortality rate using ICD-10 codes: X40-X44 AND one or more of the following in any multiple cause of death field: T40.2, T40.3, T40.4. (Numerator = deaths; Denominator = 2010 census population). Compute rates per 100,000 age adjusted using the 2000 US standard population.|Minnesota Vital Statistics||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.||||
Behavioral Health/Substance Abuse|Opioid-Related Unintentional Drug Overdose Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|30.8|Columbus, OH|Age-Adjusted rate of opioid-related mortality rate using ICD-10-CM codes: X40-X44; AND one or more of the following in any multiple cause of death: T40.2, T40.3 and T40.4 (Numerator= deaths. Denominator= 2010 census population). Rates per 100,000, age adjusted using the 2000 US standard population.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||This indicator is not exclusive of other drugs that may be included in multiple cause of death fields, such as heroin (T40.1), cocaine (T40.5), benzodiazepines (T42.4), psychostimulants with abuse potential (T43.6), other and unspecified narcotics (T40.6), or drugs not elsewhere classified (T50.9). Morphine and heroin are metabolized similarly. This may result in the over-reporting of drug poisonings associated with the effects of opioid analgesics.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||24.8|37.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|10.9|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|13.3|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2010 NC BRFSS (Mecklenburg Sample)|||||8.7|17.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|14.5|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|15.3|Fort Worth (Tarrant County), TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|16.1|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Computed as: males having five or more drinks on one occasion, females having four or more drinks on one occasion during the past 30 days|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|17.7|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. BRFSS Data. 2010. [accessed 3/9/17].|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||14.9|20.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|24.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||20.0|28.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|All|25.5|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||22.8|28.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Asian/PI|15.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||PI not included|||6.0|33.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black|1.7|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2010 NC BRFSS (Mecklenburg Sample)|||||0.0|3.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black|2.1|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black|9.5|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black|11.3|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||7.9|14.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black|11.3|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Computed as: males having five or more drinks on one occasion, females having four or more drinks on one occasion during the past 30 days|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black|24.3|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Black||Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Hispanic|10.2|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Computed as: males having five or more drinks on one occasion, females having four or more drinks on one occasion during the past 30 days|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Hispanic|13.7|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Hispanic|18.5|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||12.5|24.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Hispanic|25.9|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Hispanic|31.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||16.0|52.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Other|41.4|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|Other||Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|11.1|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|17.4|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2010 NC BRFSS (Mecklenburg Sample)|||||11.9|22.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|21.1|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|22.6|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Computed as: males having five or more drinks on one occasion, females having four or more drinks on one occasion during the past 30 days|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|25.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||20.0|31.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|37.3|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||33.2|41.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Both|White|87.5|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Female|All|5.7|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Female|All|7.9|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2010 NC BRFSS (Mecklenburg Sample)|||||4.4|11.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Female|All|9.7|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Female|All|12.2|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Computed as: males having five or more drinks on one occasion, females having four or more drinks on one occasion during the past 30 days|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Female|All|17.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||12.0|23.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Female|All|19.5|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||16.3|22.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Male|All|16.8|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Male|All|18.8|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2010 NC BRFSS (Mecklenburg Sample)|||||10.1|27.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Male|All|20.3|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Male|All|20.7|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Computed as: males having five or more drinks on one occasion, females having four or more drinks on one occasion during the past 30 days|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Male|All|31.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||24.0|38.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2010|Male|All|32.2|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||27.8|36.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|8.1|Minneapolis, MN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS SMART County Prevalence Data|Male Respondents who reported having more than 2 drinks per day, or female Respondents who reported having more than 1 drink per day.  Variable (2012, 2011): _RFDRHV4; Variable (2010): _RFDRHV3|County data was used as a proxy (Hennepin County)||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|16.0|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||10.3|21.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|16.4|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|16.8|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||12.5|22.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|17.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|18.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||16.2|21.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|18.8|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2011 NC BRFSS (Mecklenburg Sample)|||||14.8|22.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|18.9|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. BRFSS Data. 2011. [accessed 3/7/17].|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||16.0|21.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|19.1|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||16.6|21.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|19.7|Fort Worth (Tarrant County), TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|20.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|21.3|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|22.8|Long Beach, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Source: 2011 Los Angeles County Health Survey.  Note: Estimates are based on self-reported data by a random sample of 8,036 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|25.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||21.0|28.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|25.0|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|26.7|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|27.4|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|29.0|Chicago, Il|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavioral Risk Factor Surveillance System||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|All|30.5|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||25.0|36.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|American Indian/Alaska Native|14.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|American Indian alone||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|American Indian/Alaska Native|28.8|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||American Indian alone||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Asian/PI|8.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Asian/PI|12.7|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Asian/PI|12.7|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Asian/PI|14.1|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Does not include Pacific Islander||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Asian/PI|19.9|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||9.3|30.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|11.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|11.1|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|11.3|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2011 NC BRFSS (Mecklenburg Sample)|||||5.2|17.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|11.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|12.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||5.8|18.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|12.7|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|13.0|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|13.3|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||8.0|21.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|14.1|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||3.7|24.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|15.5|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||11.0|21.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|17.9|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|18.6|Chicago, Il|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavioral Risk Factor Surveillance System||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|21.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||15.0|27.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|22.8|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|26.6|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Black|33.9|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||19.2|48.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|15.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|17.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|18.0|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|21.0|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||16.2|26.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|23.9|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||17.2|30.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|25.4|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|28.2|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|28.4|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|31.5|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||16.1|47.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|33.2|Chicago, Il|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavioral Risk Factor Surveillance System||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|33.3|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic|35.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||23.0|49.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Hispanic||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Multiracial|24.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other|6.3|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||3.1|12.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other|8.0|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||2.9|13.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other|12.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other|19.8|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other|30.6|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other|33.4|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|Other||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|16.3|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||9.1|23.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|16.6|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|18.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||15.2|21.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|21.4|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||18.2|25.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|23.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|24.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|25.1|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2011 NC BRFSS (Mecklenburg Sample)|||||19.1|31.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|27.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||23.0|32.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|28.0|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|32.2|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|32.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|32.9|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|36.5|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||30.0|42.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|38.6|Chicago, Il|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavioral Risk Factor Surveillance System||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Both|White|83.8|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|7.3|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||4.1|12.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|9.1|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|11.8|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||4.9|18.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|11.9|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2011 NC BRFSS (Mecklenburg Sample)|||||7.4|16.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|12.3|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||9.9|15.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|12.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|13.5|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||10.7|16.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|16.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|18.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS ||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|19.0|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|19.5|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|20.3|Chicago, Il|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavioral Risk Factor Surveillance System||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|21.4|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|22.2|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Female|All|22.9|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||16.7|29.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|20.3|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|20.5|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||11.1|29.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|23.6|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2011 NC BRFSS (Mecklenburg Sample)|||||17.3|29.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|23.8|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||19.7|27.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|23.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|24.1|Los Angeles, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|25.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|26.0|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||22.0|30.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|27.5|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||19.8|36.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|31.4|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|32.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||27.0|38.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|32.5|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|34.4|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|38.1|Chicago, Il|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavioral Risk Factor Surveillance System||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2011|Male|All|38.5|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||29.6|47.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|7.2|Minneapolis, MN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS SMART County Prevalence Data|Male Respondents who reported having more than 2 drinks per day, or female Respondents who reported having more than 1 drink per day.  Variable (2012, 2011): _RFDRHV4; Variable (2010): _RFDRHV3|County data was used as a proxy (Hennepin County)||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|13.8|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||11.9|15.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|14.0|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2012 NC BRFSS (Mecklenburg Sample)|||||10.9|17.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|15.2|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||12.6|18.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|15.9|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||12.7|19.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|16.1|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||11.5|20.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|17.7|Fort Worth (Tarrant County), TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|18.2|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|18.5|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||14.0|24.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|18.8|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|19.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||16.0|21.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|19.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|19.6|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|22.0|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||16.7|28.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|23.1|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|25.3|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||20.7|29.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|26.8|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|All|27.1|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|American Indian/Alaska Native|33.2|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||American Indian alone||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Asian/PI|10.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Asian/PI|12.1|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Asian/PI|12.4|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||5.1|19.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Asian/PI|14.8|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Does not include Pacific Islander||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Asian/PI||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|10.3|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||2.2|18.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|10.5|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||5.1|15.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|10.6|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2012 NC BRFSS (Mecklenburg Sample)|||||4.5|16.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|10.6|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||5.4|19.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|12.0|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|14.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|14.1|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|14.3|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|14.4|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|16.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||13.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|18.9|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||13.8|25.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black|24.3|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black||San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Black||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|15.1|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||9.0|21.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|15.6|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|16.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|16.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||12.2|21.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|19.3|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||14.2|25.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|20.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||16.0|26.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|20.5|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|21.5|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|21.8|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||14.4|31.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|22.7|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|26.9|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||14.5|39.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic|27.6|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Hispanic||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other|10.8|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||4.4|24.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other|14.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other|17.5|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||5.6|29.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other|18.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other||San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|Other||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|13.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||10.8|15.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|14.8|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||11.4|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|15.8|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2012 NC BRFSS (Mecklenburg Sample)|||||11.7|19.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|16.6|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||12.5|20.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|17.9|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|18.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS |||||17.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|19.3|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||12.7|28.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|20.1|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||14.0|26.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|22.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|25.3|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|27.1|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|28.2|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|32.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|34.4|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Both|White|34.5|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||27.7|41.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|8.5|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||6.6|10.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|8.7|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||6.2|12.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|9.2|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2012 NC BRFSS (Mecklenburg Sample)|||||6.1|12.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|10.0|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||4.9|15.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|10.9|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||7.5|14.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|12.7|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|13.7|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|14.0|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|14.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|15.3|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||8.9|24.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|16.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||13.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|16.5|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||11.7|22.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|19.6|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|20.7|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|21.6|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Female|All|23.5|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||16.6|30.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|18.4|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2012 NC BRFSS (Mecklenburg Sample)|||||13.1|23.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|19.3|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||15.9|22.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|21.2|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||15.7|26.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|21.3|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||13.6|31.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|22.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||19.0|27.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|22.2|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||14.7|29.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|22.2|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||17.8|27.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|23.7|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|24.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|25.0|Baltimore, MD|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|25.8|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|27.1|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||20.4|33.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|27.2|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|28.8|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||21.1|38.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|33.0|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2012|Male|All|33.2|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|12.5|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||9.0|16.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|13.6|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||11.0|16.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|14.6|Fort Worth (Tarrant County), TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||11.0|18.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|14.6|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||12.0|17.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|14.6|San Jose, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|14.9|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||12.0|18.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|15.3|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|17.3|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||13.1|22.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|17.4|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 NC BRFSS (Mecklenburg Sample)|||||13.5|21.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|18.2|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|18.2|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|19.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||16.0|22.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|19.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|19.3|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||14.7|23.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|21.5|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|22.4|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|25.4|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||23.2|27.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|26.3|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|26.9|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|All|28.7|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||21.8|35.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|American Indian/Alaska Native|28.3|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||American Indian alone||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI|8.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI|9.3|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI|11.4|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||6.4|16.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI|12.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||3.8|21.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI|14.4|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Does not include Pacific Islander||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI|21.9|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||9.0|34.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Asian/PI||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|10.6|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||5.0|16.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|12.4|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 NC BRFSS (Mecklenburg Sample)|||||5.8|19.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|13.4|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|13.7|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|13.8|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||7.8|23.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|14.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||11.0|17.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|14.6|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|15.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|15.2|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||7.2|23.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|15.7|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||11.1|21.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|17.2|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||13.9|20.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|18.3|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black|23.6|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Black||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|14.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||7.1|21.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|17.6|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|18.1|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|18.8|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||13.1|26.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|19.0|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|20.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||14.0|26.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|22.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|22.3|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||18.0|26.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|24.8|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|25.1|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|27.2|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|28.6|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic|39.3|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||19.4|59.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Hispanic||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other|10.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other|10.9|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||1.0|20.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other|13.6|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||6.0|28.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other|19.7|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other|24.0|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other|28.7|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Native Hawaiian or other PI||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|Other||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|12.5|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||8.9|17.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|14.5|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||11.6|17.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|14.7|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|14.9|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|16.5|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||11.2|21.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|16.7|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||13.4|20.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|18.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||17.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|19.0|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|20.8|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 NC BRFSS (Mecklenburg Sample)|||||15.2|26.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|22.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|22.6|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||16.5|28.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|23.5|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||13.2|38.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|24.6|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|28.1|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||19.6|36.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|28.1|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|28.4|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|32.2|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Both|White|33.1|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||29.7|36.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|8.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||6.0|10.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|9.0|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|9.3|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||6.6|12.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|9.5|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|10.4|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||5.9|14.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|11.7|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||8.6|14.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|12.1|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|12.8|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 NC BRFSS (Mecklenburg Sample)|||||7.7|17.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|13.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||10.0|16.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|13.3|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|14.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|15.4|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||9.8|21.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|16.1|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||10.6|23.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|17.1|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|18.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|19.1|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||16.4|21.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|20.3|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||12.4|28.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Female|All|21.4|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|15.0|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||9.5|20.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|17.8|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||13.6|22.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|18.6|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||12.6|26.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|19.1|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||14.5|23.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|20.5|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 NC BRFSS (Mecklenburg Sample)|||||14.9|26.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|21.3|Houston, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Binge drinking from Houston-Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Binge drinking=More than 5 drinks on one occation for men or 4 drinks on one occasion for women.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||16.3|27.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|21.4|Phoenix, AZ|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Arizona BRFSS||All Maricopa County||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|23.6|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; defined as 5+ drinks for men and 4+ drinks for women; age adjusted percent|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|23.9|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||16.5|31.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|25.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Had binge drinking during the past 30 days (men 5+, women 4+ drinks on one occastion), age-adjusted|||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|27.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||22.0|32.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|28.0|Miami (Miami-Dade County), FL|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|28.2|Washington, DC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|DC BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|32.1|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|32.4|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Percent estimates for binge drinking  represent the percent of adults who consumed 5+ alcoholic drinks (for men) and 4+ alcoholic drink (for women ) on an occasion during the past month.  This survey is not conducted annually.||||29.1|35.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|32.8|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|33.1|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2013|Male|All|38.7|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||27.2|50.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|13.1|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||10.8|15.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|13.4|Fort Worth (Tarrant County), TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||9.4|17.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|14.7|Kansas City, MO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|500 Cities Project||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|14.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||12.1|17.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|15.1|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||11.6|18.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|16.0|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||11.6|21.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|16.2|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2014 NC BRFSS (Mecklenburg Sample)|||||12.2|20.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|16.5|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||15.4|17.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|17.6|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||14.7|20.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|19.3|Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|used crude rates|2014 BRFSS|||16.4|22.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|19.5|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||13.4|25.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|20.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||17.0|24.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|23.7|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||20.7|26.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|23.9|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||18.4|29.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|24.9|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||24.3|25.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|All|26.5|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||22.7|30.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|American Indian/Alaska Native|31.3|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||24.3|39.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI|10.5|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||0.2|20.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI|10.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||8.7|13.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI|11.3|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||4.7|24.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI|15.4|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||7.8|23.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI|15.6|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Asian alone|||13.4|18.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI||Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Asian/PI||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|6.9|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2014 NC BRFSS (Mecklenburg Sample)|||||2.1|11.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|11.4|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||4.8|24.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|12.2|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||10.3|14.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|14.5|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||9.8|20.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|15.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||11.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|15.4|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||3.9|26.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|15.8|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||6.7|24.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|17.0|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||8.5|25.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black|24.3|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||22.8|25.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Black||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|10.9|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||5.5|16.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|16.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||10.4|22.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|17.2|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||15.3|19.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|17.5|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||12.1|22.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|20.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||14.0|29.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|20.7|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||16.5|25.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic|27.8|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||26.4|29.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other|12.2|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||7.3|19.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other|13.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||2.3|25.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other|20.1|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Native Hawaiian/Other PI|||13.4|29.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other||Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other||San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|Other||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|14.0|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||11.1|16.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|14.0|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||10.5|18.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|14.8|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||11.5|18.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|17.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||16.0|18.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|18.2|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||13.4|22.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|18.5|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||11.4|25.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|22.0|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||19.6|24.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|23.2|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2014 NC BRFSS (Mecklenburg Sample)|||||16.8|29.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|25.1|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||24.3|25.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|29.2|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||25.0|33.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|31.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||26.5|35.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Both|White|43.3|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||31.4|55.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|9.7|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||6.9|12.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|9.9|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||7.2|12.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|10.2|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||7.1|14.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|11.3|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2014 NC BRFSS (Mecklenburg Sample)|||||6.7|15.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|12.8|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||11.4|14.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|13.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||10.0|17.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|14.9|Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|used crude rates|2014 BRFSS|||11.4|18.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|15.2|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||9.9|20.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|16.3|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||10.7|24.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|17.2|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||8.8|25.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|17.6|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||16.8|18.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|19.1|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||15.1|23.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|20.4|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||11.5|29.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Female|All|20.9|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||16.2|26.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|15.0|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||10.3|19.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|15.7|Detroit, MI|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|Among all adults, the proportion who reported consuming five or more drinks per occasion (for men) or 4 or more drinks per occasion (for women) at least once in the previous month.||||9.3|25.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|16.7|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||13.1|20.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|19.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||15.2|24.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|20.7|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2014 NC BRFSS (Mecklenburg Sample)|||||14.3|27.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|20.7|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||19.1|22.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|22.5|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||13.2|31.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|24.0|Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|used crude rates|2014 BRFSS|||19.5|28.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|25.5|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||20.9|30.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|27.3|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||19.4|35.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|27.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||23.3|32.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|28.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||22.0|35.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|31.5|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||25.8|37.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2014|Male|All|32.8|U.S. Total, U.S. Total|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Binge drinking in past month, Adults (percent, 18+ years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||31.8|33.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|12.9|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||9.6|17.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|13.9|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||10.9|16.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|14.4|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2015 NC BRFSS (Mecklenburg Sample)|||||10.3|18.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|16.1|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||13.5|18.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|17.2|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||16.1|18.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|17.3|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||13.4|21.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|17.9|Fort Worth (Tarrant County), TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||12.6|23.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|18.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||14.0|22.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|18.7|Long Beach, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Source: 2015 Los Angeles County Health Survey.  Note: Estimates are based on self-reported data by a random sample of 8,008 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|19.5|Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||16.2|22.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|22.3|Kansas City, MO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|23.3|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||16.5|30.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|24.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||21.1|27.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|24.2|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||21.7|26.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|26.6|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||21.5|31.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|All|37.2|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||30.1|44.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI|7.0|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||5.4|9.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI|9.0|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||4.3|13.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI|13.4|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||0.6|26.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI|17.0|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||10.1|27.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI||Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI||Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Asian/PI||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|12.7|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||10.8|14.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|13.2|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||4.8|21.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|14.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||10.0|18.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|14.2|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2015 NC BRFSS (Mecklenburg Sample)|||||6.7|21.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|18.2|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||14.1|22.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|18.7|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||6.5|30.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|22.2|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||11.3|39.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|27.6|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||7.7|47.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black|28.4|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||14.0|42.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black||Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black||San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Black||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|13.4|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||7.3|19.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|15.3|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||10.6|21.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|17.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||10.0|27.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|17.6|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||7.9|27.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|18.1|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||16.3|20.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|18.1|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||13.5|22.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|18.2|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||13.3|23.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|23.8|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||13.3|38.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic|54.0|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||32.9|75.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Hispanic||Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other|10.1|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS||Includes Asian/PI|||0.0|22.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other|19.7|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||1.6|37.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other|24.4|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||17.7|32.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate as statistically unstable.; Data is for Alameda County. ||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|Other||Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|9.1|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||5.3|15.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|14.0|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||10.2|17.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|16.5|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2015 NC BRFSS (Mecklenburg Sample)|||||10.3|22.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|17.8|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||14.1|21.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|18.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||16.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|18.4|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||13.7|23.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|23.9|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||21.6|26.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|26.7|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||23.4|30.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|28.2|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||19.0|37.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|31.8|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||27.7|35.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|32.8|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||25.9|39.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White|46.6|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||33.9|59.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Both|White||Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|7.9|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||5.1|10.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|8.7|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||5.4|13.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|11.4|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||8.1|14.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|12.8|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||11.5|14.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|13.7|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2015 NC BRFSS (Mecklenburg Sample)|||||8.5|18.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|14.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||10.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|14.8|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||9.1|20.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|15.4|Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||11.1|19.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|18.7|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||11.0|26.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|20.2|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||16.9|23.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|22.1|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||18.0|26.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All|33.6|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||23.9|43.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Female|All||Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|12.9|Charlotte, NC|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|2015 NC BRFSS (Mecklenburg Sample)|||||7.3|18.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|17.7|San Antonio, TX|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS survery data||Bexar County level data|||12.1|25.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|20.1|Las Vegas (Clark County), NV|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Nevada BRFSS - Clark County|||||14.9|25.2
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|20.8|San Diego County, CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion) (variable calculated from one or more BRFSS questions)||||16.9|24.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|22.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||16.0|28.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|22.3|New York City, NY|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||20.6|24.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|23.7|Indianapolis (Marion County), IN|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||17.3|30.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|24.3|Portland (Multnomah County), OR|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||19.3|29.3
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|25.9|Seattle, WA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||21.8|30.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|28.1|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||17.1|39.1
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|28.8|Boston, MA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||24.9|32.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|36.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||29.1|44.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2015|Male|All|41.1|Oakland (Alameda County), CA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|California Health Interview Survey (AskCHIS)|Males are considered binge drinkers if they consumed 5 or more alcoholic drinks on at least one occassion in the past year. Females are considered binge drinkers if they consumed 4 or more alcoholic drinks on at least one occasion in the past year.|Binge drinking in the past year.  Data is for Alameda County. |||29.4|52.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|All|20.7|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||15.7|25.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|All|24.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||20.0|28.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|All|25.4|Kansas City, MO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|All|26.5|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||23.4|29.6
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Asian/PI||Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Black|9.3|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||3.2|15.4
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Black|9.6|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||2.4|16.8
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Black|15.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||12.0|19.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Hispanic|23.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||17.0|30.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Hispanic|25.5|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||19.4|31.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Hispanic||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Other|17.2|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS||Includes Asian/PI|||7.4|26.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|Other||Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|White|20.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||18.0|21.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|White|27.2|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||20.0|34.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Both|White|30.7|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||26.4|34.9
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Female|All|17.9|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||14.1|21.7
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Female|All|19.8|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||12.6|27.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Female|All|20.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||16.0|25.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Male|All|21.5|Columbus, OH|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|BRFSS|||||14.5|28.5
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Male|All|29.0|Philadelphia, PA|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|PA Eddie-->BRFSS |||||23.0|35.0
Behavioral Health/Substance Abuse|Percent of Adults Who Binge Drank|2016|Male|All|35.2|Denver, CO|BRFSS (or similar) How many times during the past month did you have 5 (men) (4 for women) or more drinks on one occasion in past 30 days?|Colorado BRFSS|Colorado BRFSS||||30.4|39.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|All|19.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||16.0|22.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|All|19.7|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|Asian/PI|12.0|Seattle, WA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||10.0|14.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|Black|13.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||10.0|17.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|Black|13.4|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|Hispanic|21.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||16.0|26.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|Hispanic|25.8|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|Other|19.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||13.0|26.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Both|White|23.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||18.0|29.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Female|All|18.9|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2010|Male|All|20.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||17.0|24.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|7.7|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|7.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|8.2|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.0|9.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|11.7|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||10.0|13.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|12.7|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|15.2|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|15.6|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||13.6|17.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|16.6|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||13.3|19.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|17.5|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|All|33.0|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||30.1|36.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|American Indian/Alaska Native|9.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||American Indian alone||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|American Indian/Alaska Native|15.8|San Diego County, CA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Asian/PI|4.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Does not include Pacific Islander||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Asian/PI|4.2|San Diego County, CA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Asian/PI|5.0|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Asian/PI|5.5|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||4.1|7.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Asian/PI|7.5|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Asian/PI|15.2|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||7.2|23.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|4.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|6.1|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|7.8|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||6.6|9.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|9.0|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|9.1|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|9.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||7.6|12.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|10.5|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|10.7|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||7.3|14.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|11.5|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black|22.8|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||18.2|28.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|7.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|7.9|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|12.0|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.4|18.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|12.6|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|16.3|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||11.9|20.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|16.9|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|18.5|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|20.4|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|20.6|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||15.7|26.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|22.0|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||16.9|28.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Hispanic|37.7|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||34.4|41.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Multiracial|8.9|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Other|3.4|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Other|8.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Native Hawaiian or other PI||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Other|11.6|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Other|11.7|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||6.6|19.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|Other|20.2|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|9.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|10.8|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|11.0|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|18.7|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|21.6|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||17.2|26.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|30.6|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||21.7|41.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|30.9|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|30.9|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|35.7|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||24.4|46.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Both|White|40.2|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||31.2|50.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|6.9|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|7.8|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||6.2|9.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|8.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|10.0|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||7.7|13.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|10.4|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|12.7|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|13.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||11.6|16.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|14.6|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|16.5|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||11.7|21.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|17.8|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Female|All|31.3|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||27.3|35.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|7.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|8.4|Los Angeles, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Los Angeles Unified School District data only. National Survey on Drug Use and Health (NSDUH), 2002-2011.|Percent reporting having consumed 5 or more drinks on the same occasion on at least 1 day in the past 30 days. Annual population based survey by computer assisted self interview, computer assisted personal interview, and computer assisted self interview.                          Study population  includes civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters.|Estimates obtained from the restricted-use data analysis system (R-DAS) query, which is available for 10-year aggregated data. We used this 10-year aggregated data as annual NSDUH report does not provide County level gender and racial/ethnic estimates (no city level data is available).  Youth are defined as age 12-17 years. Prevalence of underage drinking for white youth age 12-20 is 31.6%||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|8.5|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||6.8|10.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|11.3|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in the past 30 days on which they consumed five or more drinks of alcohol within a couple of hours; Data was pooled for 2011-2013.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|12.5|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|13.0|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||10.7|15.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|15.6|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|16.8|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||12.6|21.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|16.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||14.0|20.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|17.1|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2011|Male|All|34.5|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||30.3|39.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|All|3.3|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|YRBSS was not used since it is only representative of San Diego City (not San Diego County) 2012-2014 (Pooled|||1.7|5.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|All|7.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|All|20.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||17.0|23.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|American Indian/Alaska Native|7.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||American Indian alone||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Asian/PI|2.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Does not include Pacific Islander||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Asian/PI|12.0|Seattle, WA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||10.0|15.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. 2012-2014 (Pooled)/San Diego County||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Black|4.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Black|18.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||13.0|25.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. 2012-2014 (Pooled)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Hispanic|7.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Hispanic|19.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||16.0|23.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Hispanic||San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. 2012-2014 (Pooled)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Other|19.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||11.0|30.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. 2012-2014 (Pooled)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|White|9.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|White|24.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||20.0|29.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Both|White||San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. 2012-2014 (Pooled)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Female|All|4.9|San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|YRBSS was not used since it is only representative of San Diego City (not San Diego County) 2012-2014 (Pooled|||2.0|7.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Female|All|8.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Female|All|17.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||14.0|21.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Male|All|7.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Male|All|22.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||18.0|26.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2012|Male|All||San Diego County, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of all adolescents 12 to 17 years of age. Male binge drinking is five or more drinks on one occasion in past month, female binge drinking is four or more drinks.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. 2012-2014 (Pooled)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|6.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|8.9|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.1|11.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|10.4|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||8.8|12.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|10.8|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|11.8|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||11.2|11.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|12.3|Washington, DC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|DC YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|12.4|Baltimore, MD|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBSS for location local Baltimore, MD||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|13.9|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|14.9|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||12.4|17.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|15.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||12.9|17.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|16.7|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||14.2|19.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|17.6|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|17.8|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|19.4|San Antonio, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|2013 BRFSS Bexar County||Data includes Bexar County, TX, not just San Antonio; During the past 30 days, on how many days did you have 5 or more drinks of alcohol in a row, that is, within a couple of hours?||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|19.6|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|All|31.0|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||27.0|35.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|American Indian/Alaska Native|6.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||American Indian alone||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|3.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Does not include Pacific Islander||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|3.9|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|4.3|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||3.2|5.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|5.1|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|6.7|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||2.8|10.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|7.1|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Asian/PI|8.6|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||8.0|9.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|4.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|7.2|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||6.8|7.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|8.0|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|8.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||3.2|13.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|9.2|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.3|11.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|10.2|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|10.7|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||8.2|13.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|10.7|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|10.9|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|11.1|Baltimore, MD|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBSS for location local Baltimore, MD||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|11.2|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|11.3|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||7.8|14.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black|28.5|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||20.6|38.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|6.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|7.5|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||3.8|14.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|14.5|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||13.7|15.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|14.7|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|15.3|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||12.1|19.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|16.1|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|17.3|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||12.7|23.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|18.8|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|19.2|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||15.1|23.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|20.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||17.1|24.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|21.0|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|21.9|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Hispanic|32.3|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||27.8|37.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Multiracial|19.2|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Other|3.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Native Hawaiian or other PI||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Other|9.1|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|Other|14.7|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||13.3|16.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|7.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|11.7|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||8.2|15.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|14.1|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|20.3|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||18.0|22.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|21.5|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||12.6|30.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|23.8|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|24.0|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|24.3|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|24.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||19.2|31.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|28.9|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||20.5|39.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Both|White|30.1|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|6.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|9.9|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.8|12.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|10.3|Baltimore, MD|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBSS for location local Baltimore, MD||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|10.4|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|10.4|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||8.3|12.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|10.6|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||10.2|11.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|14.6|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||11.9|17.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|14.6|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|14.7|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||11.9|17.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|15.4|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||12.0|18.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|16.1|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|18.8|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|19.6|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Female|All|29.2|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||24.7|34.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|6.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|7.4|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||5.0|10.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|10.1|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||8.1|12.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|11.0|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as 5+ drinks for both men and women; percent|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|12.5|Oakland (Alameda County), CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|California Healthy Kids Survey (CHKS), 2013-2015|Numerator= 1-30 days of 5+ drinks in a row. Denominator= total number CHKS surveyed of group High Schoolers grades 9-12th in Oakland Unified School District|Value is for the range of school years, 2013-2015.|||12.0|13.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|13.2|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|13.4|Baltimore, MD|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBSS for location local Baltimore, MD||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|14.1|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, Centers for Disease Control and Prevention|This survey is conducted every other year.  Estimates for binge drinking represent public high school students who had 5+ drinks in a row within couple of hours during the past 30 days.||||11.1|17.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|15.3|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||12.1|18.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|16.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|19.1|Chicago, Il|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|19.2|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||15.8|23.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|20.0|Denver, CO|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]. 5 or more drinks in a row within a couple hours on at least 1 day in past 30|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2013|Male|All|32.4|Houston, TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Yourth Risk Behavior Surveillance System (YRBSS) from Center for Disease Control and Prevention. http://nccd.cdc.gov/youthonline. Accessed at May 21, 2015.|Current alcohol use in Houston. Current drank alcohol: at least one drink of alcohol on at least 1 day during the 230 days before the survey. N/A=<100 respondents for the subgroup. Survey for High school student including 9th-12th grades.|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||27.3|38.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|All|6.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||5.6|6.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|All|16.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||13.0|19.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|American Indian/Alaska Native|12.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||5.8|25.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Asian/PI|1.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Asian alone|||0.8|3.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Asian/PI|9.0|Seattle, WA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||6.0|13.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Black|3.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||2.9|5.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Black|11.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||9.0|14.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Hispanic|5.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||4.8|6.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Hispanic|16.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||14.0|19.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Other|4.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA||Native Hawaiian/Other PI|||1.3|17.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|Other|21.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||14.0|31.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|White|7.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||6.6|8.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Both|White|19.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||15.0|25.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Female|All|5.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||5.1|6.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Female|All|14.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||12.0|18.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Male|All|6.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month, Adolescents (percent, 12-17 years), National Survey on Drug Use and Health (NSDUH), SAMHSA|||||5.7|7.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2014|Male|All|17.0|Seattle, WA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Healthy Youth Survey |||||13.0|21.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|8.5|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||7.3|9.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|8.8|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||7.2|10.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|9.0|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.1|11.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|10.8|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||8.6|13.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|10.9|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||11.4|14.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|14.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||12.1|15.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|14.5|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||13.0|16.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|14.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||12.5|17.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|14.9|Portland (Multnomah County), OR|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Oregon Healthy Teens|weighted % of 11th graders who binge drank at least one day (5+ drinks in a row) in the last 30 days|2015 Oregon Healthy Teens, 11th graders|||12.1|17.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|16.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|All|17.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||15.8|19.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|American Indian/Alaska Native|33.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||19.7|50.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI|3.2|New York City, NY|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||2.2|4.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI|3.7|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||2.3|5.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI|5.6|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||3.5|8.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI|7.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||4.0|14.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI|8.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS||Asian alone|||5.8|11.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; Fort Worth Independent School District (not all of Tarrant County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District; 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for Fort Worth Independent School District.||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|4.7|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||2.9|6.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|6.4|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||4.4|9.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|7.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||3.6|10.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|7.6|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||5.1|11.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|8.2|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||6.5|10.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|8.7|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||6.3|11.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|8.8|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||6.5|11.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|9.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black|11.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||8.8|14.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|10.1|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||8.5|12.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|12.5|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||8.9|17.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|14.4|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||9.7|20.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|14.5|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||10.3|20.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|15.2|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||8.5|25.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|15.4|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||12.5|18.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|16.0|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||14.0|18.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|16.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||13.4|18.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|17.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||15.8|19.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Hispanic|18.3|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Multiracial|18.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||15.1|22.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Other|9.0|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||5.8|13.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Other||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; Fort Worth Independent School District (not all of Tarrant County)||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|Other||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District; 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for Fort Worth Independent School District.||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|14.9|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||10.6|20.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|15.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||11.7|19.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|16.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|17.2|Philadelphia, PA|YRBS/YRBSS (or similar). During the past 30 days| on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more drinks of alcohol in a row (male)?|YRBS|||||11.3|25.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|18.7|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||14.5|23.9
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|19.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||16.8|23.0
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|22.5|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||14.9|30.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|24.5|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||19.5|30.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Both|White|28.3|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||19.4|39.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|9.1|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||7.6|10.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|9.7|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||7.5|12.4
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|9.7|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||7.6|12.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|10.7|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||8.4|13.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|11.1|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||8.4|13.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|13.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||11.4|16.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|14.1|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||11.9|16.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|14.3|Portland (Multnomah County), OR|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Oregon Healthy Teens|weighted % of 11th graders who binge drank at least one day (5+ drinks in a row) in the last 30 days|2015 Oregon Healthy Teens, 11th graders|||11.0|17.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|15.7|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|16.6|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||13.6|19.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Female|All|16.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||14.4|19.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|7.8|New York City, NY|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||6.6|9.3
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|7.9|Detroit, MI|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|Drank 5 or more drinks of alcohol in a row - within a couple of hours on at least 1 day during the 30 days before the survey||||5.8|10.7
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|8.1|San Francisco, CA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||6.2|10.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|10.8|Boston, MA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||8.1|13.5
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|10.8|Philadelphia, PA|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|YRBS|||||8.1|14.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|11.6|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Nevada YRBS Clark County|||||9.3|13.8
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|14.6|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||12.4|17.1
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|15.6|Portland (Multnomah County), OR|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Oregon Healthy Teens|weighted % of 11th graders who binge drank at least one day (5+ drinks in a row) in the last 30 days|2015 Oregon Healthy Teens, 11th graders|||13.0|18.2
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|15.9|Charlotte, NC|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey||||||12.7|19.6
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|16.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Behavioral Health/Substance Abuse|Percent of High School Students Who Binge Drank|2015|Male|All|18.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar). Consuming five or more alcoholic drinks within a couple of hours during the 30 days before the survey|Binge drinking in past month (5 or more drinks of alcohol in a row, at least 1 day during the 30 days before survey), Adolescents (percent, 9-12 grades), High School YRBS|||||16.9|20.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|88.5|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|98.0|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|140.1|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|149.9|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||145.3|154.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|156.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||146.5|167.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|159.9|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||153.5|166.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|161.3|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|162.2|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|167.6|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|169.3|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||163.2|175.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|172.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|172.2|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||158.9|185.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|172.8|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|176.1|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|184.1|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|185.1|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||175.9|194.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|189.4|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|193.7|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|195.4|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|196.9|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||184.6|209.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|204.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|PA Eddie-->Vital Statistics|||||197.7|212.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|22.3|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|122.4|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|404.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|59.2|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|79.8|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|89.9|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||71.0|113.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|97.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C101|PA Eddie-->Vital Statistics|||||87.4|140.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|101.7|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||66.4|149.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|106.9|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|107.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|108.9|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|110.1|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||94.0|126.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|120.6|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|131.1|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||124.5|138.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|148.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|153.7|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||127.6|179.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|156.0|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|91.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|176.7|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||149.8|203.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|178.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|196.1|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||173.9|218.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|204.9|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|207.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|207.7|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||182.8|232.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|208.8|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|209.5|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||189.2|231.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|211.0|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|211.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|214.4|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|221.8|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|222.1|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||200.6|245.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|224.2|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||180.3|276.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|225.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|226.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||199.1|252.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|238.7|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|241.0|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C99|PA Eddie-->Vital Statistics|||||228.8|253.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|241.1|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|244.8|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|0.0|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|83.1|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|86.3|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||61.0|118.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|89.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||77.2|102.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|97.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||48.3|178.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|106.5|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|106.9|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|113.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C100|PA Eddie-->Vital Statistics|||||78.5|117.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|116.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|117.2|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|119.7|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|120.4|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|127.9|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|130.7|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|132.2|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||118.6|147.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|142.1|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||133.0|151.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|144.6|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|166.6|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|0.0|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|374.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|1.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|34.4|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||10.6|58.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|82.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|103.5|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|108.6|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|120.3|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|152.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race|||71.6|298.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|56.6|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|136.1|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|156.9|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|160.1|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||152.8|168.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|162.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|164.5|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||140.8|188.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|164.9|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||152.5|178.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|167.2|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|172.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|174.1|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||164.1|184.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|176.5|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|176.9|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|178.7|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|180.0|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||169.5|191.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|180.7|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|186.5|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|189.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||182.1|197.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|190.2|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|191.6|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|198.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C98|PA Eddie-->Vital Statistics|||||188.8|208.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|202.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||187.1|217.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|77.4|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|101.9|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|112.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|121.5|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||110.6|132.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|128.3|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||122.6|134.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|133.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||120.8|147.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|134.4|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|138.6|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|141.3|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|142.1|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|146.7|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|147.6|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||139.8|155.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|149.2|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||132.7|165.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|151.8|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|156.6|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|158.3|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||147.2|170.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|168.0|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|168.9|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C103|PA Eddie-->Vital Statistics|||||160.5|177.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|170.3|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|173.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||157.9|188.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|173.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|93.2|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|104.0|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|173.3|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||157.3|187.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|178.6|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|178.6|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2010-2012|||171.1|186.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|190.2|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||173.0|208.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|193.1|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|196.4|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||186.8|205.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|203.3|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|205.6|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|206.3|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||183.8|228.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|209.9|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Overall cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|211.5|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|218.6|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|223.9|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|226.8|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||210.8|243.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|227.4|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|230.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|230.4|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|234.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||212.8|255.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|260.0|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C104|PA Eddie-->Vital Statistics|||||247.3|272.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|85.9|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|100.0|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|137.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|138.9|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|146.1|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|155.2|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|157.3|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|158.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|158.7|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|158.7|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|160.0|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|163.6|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|169.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|171.5|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|172.4|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|174.1|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|175.4|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||169.2|181.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|176.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||166.3|186.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|181.0|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|182.5|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|191.4|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|192.2|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||182.8|202.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|196.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||183.8|208.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|213.3|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|215.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C105|PA Eddie-->Vital Statistics|||||208.4|223.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|13.5|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|109.4|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|222.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||American Indian alone||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|335.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|59.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|78.3|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|103.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||76.4|136.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|103.4|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|104.7|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|105.6|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|105.8|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|116.8|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|119.0|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|123.7|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|124.4|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C109|PA Eddie-->Vital Statistics|||||97.9|150.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|128.1|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|130.9|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|137.7|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||118.8|156.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|153.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|179.0|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|96.5|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|164.0|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|172.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|180.2|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|184.4|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|194.9|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|200.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||177.4|223.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|202.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|204.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|209.1|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|210.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||164.6|264.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|212.7|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|213.9|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|213.9|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|214.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|214.6|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||189.5|239.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|215.4|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|217.6|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|222.4|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|225.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|230.4|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|232.1|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|232.7|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|241.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C107|PA Eddie-->Vital Statistics|||||229.6|253.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|33.4|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|63.6|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|79.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||44.4|130.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|86.4|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|98.3|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||85.4|111.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|104.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|111.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|113.8|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|115.2|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|117.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|117.4|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|118.3|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|120.0|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|121.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|121.8|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|126.3|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|132.2|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|133.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C108|PA Eddie-->Vital Statistics|||||109.9|157.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|135.1|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|138.1|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|145.2|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|160.5|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|12.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|20.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|109.9|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|164.8|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Consider omission due to wide confidence intervals and underreporting of this category.  Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subj||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|174.9|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|0.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|47.5|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|55.3|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||27.3|83.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|109.9|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|123.5|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|124.6|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|129.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|145.2|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|55.6|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|118.8|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|140.8|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|147.4|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|148.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|150.0|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|151.1|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|156.2|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|161.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|163.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|164.3|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|173.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|173.1|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|173.8|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|175.0|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|176.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|177.8|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|180.1|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|180.4|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|180.6|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||169.7|191.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|183.4|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|193.5|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||185.7|201.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|196.3|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||181.6|211.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|205.4|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C106|PA Eddie-->Vital Statistics|||||195.6|215.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|77.0|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|101.8|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|114.2|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|117.7|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|128.0|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|130.6|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|132.6|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|132.8|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|136.8|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|138.2|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|138.5|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|138.5|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|141.2|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|141.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|141.5|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|144.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|153.6|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||145.7|161.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|154.4|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|157.5|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||144.8|170.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|161.9|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||150.7|173.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|163.2|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|163.3|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|171.1|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||156.1|186.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|178.3|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|186.2|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C111|PA Eddie-->Vital Statistics|||||177.4|195.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|97.0|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|98.7|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|171.6|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|174.5|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|186.3|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|186.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|187.8|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|189.1|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|190.6|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|195.2|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|200.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|204.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2011, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|204.4|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||194.6|214.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|204.9|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||188.2|221.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|213.5|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|217.7|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|219.2|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|220.8|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|223.1|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|225.7|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|232.1|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|233.8|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||212.8|254.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|239.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||223.1|256.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|264.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C112|PA Eddie-->Vital Statistics|||||252.0|277.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|266.4|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|83.1|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|109.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|134.2|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|137.5|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|139.6|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|150.6|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|152.9|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|155.1|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|158.3|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|160.3|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|166.5|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|168.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|173.0|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||166.8|179.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|173.1|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||163.3|182.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|174.3|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|177.6|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|179.5|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|186.1|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|187.3|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|193.5|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|194.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||181.9|206.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|196.3|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||186.9|206.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|207.1|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C113|PA Eddie-->Vital Statistics|||||200.0|214.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|213.9|Baltimore, MD|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|217.6|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|398.4|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||398.4|367.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|39.5|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|111.4|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|184.5|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||American Indian alone||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|451.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|60.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|75.7|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|82.4|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|100.0|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C117|PA Eddie-->Vital Statistics|||||76.4|123.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|101.5|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|104.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|108.3|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|108.4|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|120.9|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|122.6|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|123.9|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|127.0|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||109.6|144.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|132.4|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|137.3|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||137.3|113.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|143.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||110.6|182.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|146.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|111.5|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|158.2|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|165.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|171.0|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|178.6|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|192.8|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||169.1|216.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|198.6|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|201.5|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||178.3|224.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|206.4|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|207.2|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|209.9|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|210.4|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||210.4|184.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|210.7|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|213.8|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|217.5|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|217.7|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|222.5|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|225.5|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|230.9|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|232.3|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||210.6|255.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|235.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C115|PA Eddie-->Vital Statistics|||||223.9|247.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|240.1|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|246.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|247.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|252.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||201.8|311.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|36.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|72.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|75.8|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|81.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|107.8|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|116.9|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|119.4|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|123.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|125.7|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||110.3|141.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|125.9|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|126.9|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|130.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|131.1|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|131.8|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|135.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|136.7|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|138.3|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|138.4|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||138.4|105.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|158.8|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|162.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C116|PA Eddie-->Vital Statistics|||||136.6|188.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|196.5|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|9.4|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|24.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|59.8|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|234.7|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Consider omission due to wide confidence intervals and underreporting of this category.  Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subj||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|1.9|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|40.7|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||20.1|61.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|89.0|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|102.0|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|109.1|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|130.7|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||130.7|67.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|157.6|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|57.6|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|128.4|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|146.2|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|151.7|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|151.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|156.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|157.7|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|158.6|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|166.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|169.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|169.9|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|170.6|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|171.6|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||171.6|148.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|171.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||161.2|182.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|173.9|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|175.3|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|176.0|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|177.2|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C114|PA Eddie-->Vital Statistics|||||168.1|186.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|177.6|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|178.5|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|181.6|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|186.1|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||178.5|193.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|192.0|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||181.2|203.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|200.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|202.2|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||187.3|217.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|72.4|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|109.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|114.7|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|115.9|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|116.2|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|128.3|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|132.0|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|135.6|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|137.5|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|140.3|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|142.1|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|142.2|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|145.8|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|146.8|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||130.8|162.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|148.0|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|149.5|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||137.3|161.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|153.1|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||145.2|161.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|158.2|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|158.3|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||147.3|170.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|160.0|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|161.5|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|162.3|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|163.9|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||149.2|178.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|174.9|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C119|PA Eddie-->Vital Statistics|||||166.4|183.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|187.3|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|97.7|Los Angeles, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|107.4|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|162.3|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|167.6|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|173.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|181.3|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|184.8|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|186.3|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|186.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for crude rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|188.6|Houston, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|198.7|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||189.0|208.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|200.3|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, 2012, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|201.9|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||202.0|180.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|202.3|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|207.0|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||190.4|223.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|215.8|Washington, DC|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|216.8|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|220.5|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|223.1|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|227.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|241.3|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|241.8|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||220.5|263.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|253.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||236.8|271.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|259.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C120|PA Eddie-->Vital Statistics|||||247.1|272.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|264.2|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|108.4|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|130.9|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|133.5|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|134.4|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|143.9|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|147.2|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||142.6|151.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|151.3|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|151.6|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|155.5|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||149.2|161.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|155.6|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|161.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||148.3|173.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|163.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|172.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||162.5|182.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|175.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|177.6|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|178.6|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|179.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||170.6|188.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|180.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||173.9|186.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|187.2|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|189.7|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|197.5|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C121|PA Eddie-->Vital Statistics|||||190.6|204.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|200.7|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||188.4|213.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|210.6|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|20.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|110.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|180.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|453.5|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||American Indian alone||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|69.7|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|77.0|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|87.0|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C125|PA Eddie-->Vital Statistics|||||65.5|108.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|88.4|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|100.5|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|102.8|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|104.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|108.1|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||81.2|141.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|113.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|130.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||100.5|167.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|131.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|132.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||108.9|155.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|135.2|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||128.5|142.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|136.4|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|142.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||124.0|161.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|145.9|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|107.3|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|154.7|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|165.3|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|169.6|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|172.5|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|185.2|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|188.5|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||166.8|210.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|189.4|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|191.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|194.4|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|198.3|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|200.3|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||177.2|223.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|214.3|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||193.9|236.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|215.4|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||190.2|240.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|219.1|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|220.5|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C123|PA Eddie-->Vital Statistics|||||209.1|231.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|222.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||195.4|248.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|223.6|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|223.8|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||202.9|246.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|227.1|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|241.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||191.9|299.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|248.9|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|39.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|78.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|90.9|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|105.0|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||89.4|120.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|107.4|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|114.5|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|118.4|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|123.2|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|126.9|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|127.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C124|PA Eddie-->Vital Statistics|||||106.5|148.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|129.9|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|132.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|133.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||82.8|204.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|134.4|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|135.2|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||121.6|150.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|138.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||122.1|154.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|141.4|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||106.8|183.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|147.8|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|177.2|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|27.5|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|79.7|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|94.0|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|104.5|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Consider omission due to wide confidence intervals and underreporting of this category.  Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subj||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|124.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C126|PA Eddie-->Vital Statistics|||||68.6|180.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|0.2|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|47.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||23.3|71.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|51.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|86.9|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|109.5|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||52.5|201.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|131.7|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||111.4|152.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|138.7|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|144.5|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||137.5|152.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|145.3|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|148.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|156.2|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|156.8|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|157.2|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|163.0|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||155.5|170.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|166.5|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|167.7|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|170.3|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|172.2|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||161.8|182.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|173.2|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C122|PA Eddie-->Vital Statistics|||||164.3|182.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|174.2|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|174.5|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|175.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||165.2|186.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|181.9|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|188.7|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|191.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||184.1|199.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|196.8|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|201.2|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||186.3|216.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|107.7|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|108.0|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|115.0|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|118.1|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|123.1|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||117.5|129.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|124.4|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|131.0|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|131.1|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|131.3|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|135.7|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||128.0|143.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|139.5|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|141.1|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||125.4|156.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|146.9|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|148.9|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||136.9|160.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|149.9|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|150.8|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||140.0|162.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|151.1|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|154.2|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|157.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||149.2|165.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|162.0|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|174.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C127|PA Eddie-->Vital Statistics|||||166.3|183.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|175.4|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||160.2|190.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|182.9|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|107.1|Phoenix, AZ|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|148.4|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|165.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|169.7|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|All Cancer Mortality Rate ICD-10 codes: C00-C97, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|171.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|180.0|San Francisco, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||Value is reported for a multi-year period, 2013-2015|||172.5|187.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|181.5|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended;age-adjusted rate per 100,000 population.|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|184.7|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||174.0|195.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|188.5|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||167.4|209.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|188.6|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|189.7|San Jose, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|196.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|209.3|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||192.5|226.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|209.7|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||199.7|219.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|215.2|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Colorado Vital Records Death Data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|216.9|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|219.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|222.6|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||207.2|239.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|226.4|Chicago, Il|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|238.3|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|240.1|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C128|PA Eddie-->Vital Statistics|||||228.2|252.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|240.3|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||219.1|261.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|252.5|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MDHHS||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|134.0|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|136.4|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|146.5|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|148.5|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|151.6|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||145.6|157.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|153.1|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|153.7|Oakland (Alameda County), CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|156.1|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||149.9|162.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|159.1|Houston, TX|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||Harris County data, not just Houston|||154.8|163.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|160.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|160.8|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||151.5|170.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|161.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|180.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|180.6|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||171.7|189.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|189.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||182.7|195.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|192.1|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C00-C97||||182.6|201.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|195.1|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|200.1|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C129|PA Eddie-->Vital Statistics|||||193.1|207.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|204.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||191.7|216.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|218.3|Cleveland, OH|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|106.7|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|310.2|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|96.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||72.0|127.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|98.9|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|101.9|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||77.2|132.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|102.0|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|102.0|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|102.8|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|105.0|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C133|PA Eddie-->Vital Statistics|||||82.0|127.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|112.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||92.6|135.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|113.2|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics|All cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97||||92.2|134.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|119.1|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||85.5|161.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|122.3|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|146.4|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||127.3|165.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|162.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|150.9|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|155.0|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|156.8|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||123.2|197.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|159.8|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|166.3|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|180.2|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||155.0|205.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|182.3|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||161.9|202.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|190.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|197.6|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C00-C97||||187.2|208.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|205.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||182.9|228.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|206.0|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||162.6|257.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|214.6|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics|All cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97||||188.0|241.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|214.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C131|PA Eddie-->Vital Statistics|||||203.5|225.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|217.9|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||197.5|240.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|221.8|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||196.2|247.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|224.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|243.5|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|270.6|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|40.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|84.6|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||47.3|146.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|88.1|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||54.5|134.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|96.9|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|106.7|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||91.0|122.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|112.4|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|115.1|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|115.9|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|128.2|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|129.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C132|PA Eddie-->Vital Statistics|||||107.8|151.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|130.1|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||114.8|145.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|132.4|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||124.3|140.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|137.1|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C00-C97||||90.6|183.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|149.3|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics|All cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97||||115.2|190.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|164.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|93.5|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C134|PA Eddie-->Vital Statistics|||||46.2|140.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|143.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|189.8|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Consider omission due to wide confidence intervals and underreporting of this category.  Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subj||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|27.6|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||10.5|44.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|63.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|171.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||58.5|429.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|132.7|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics|All cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97||||112.2|153.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|140.5|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|143.3|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||132.1|155.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|145.4|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|149.9|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C00-C97||||126.9|172.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|152.9|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|164.1|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||154.0|174.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|165.3|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||157.8|172.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|166.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|166.3|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||156.8|175.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|168.3|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|169.2|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|173.1|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|173.7|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|176.2|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||166.0|187.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|184.5|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C130|PA Eddie-->Vital Statistics|||||175.2|193.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|204.5|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||196.4|212.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|205.8|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||190.8|220.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|111.3|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|112.4|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||102.6|122.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|115.5|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|124.5|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C98|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||112.8|137.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|126.8|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|128.5|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|128.6|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics|All cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97||||113.6|143.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|129.7|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|130.8|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||123.3|138.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|132.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|136.8|Houston, TX|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||Harris County data, not just Houston|||131.5|142.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|138.1|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|140.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|142.2|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||130.5|153.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|156.2|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||145.4|167.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|161.2|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|163.5|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||155.3|171.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|166.0|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||151.3|180.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|168.1|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C00-C97||||156.4|179.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|186.2|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C135|PA Eddie-->Vital Statistics|||||166.3|183.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|161.2|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||147.1|175.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|161.3|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||146.0|177.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|165.8|Long Beach, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|166.1|Miami (Miami-Dade County), FL|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|173.1|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|176.7|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|188.6|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|189.2|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County Vital Statistics|All cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97||||168.4|210.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|190.0|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder: C00-C97|||||174.3|205.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|190.7|Houston, TX|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||Harris County data, not just Houston|||183.3|198.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|192.6|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||181.8|203.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|192.9|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C00-C97, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|201.8|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|218.2|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||203.0|234.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|223.5|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||213.1|233.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|231.3|Detroit, MI|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C00-C97||||215.3|247.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|232.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|247.9|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|259.7|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||237.8|281.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|264.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C136|PA Eddie-->Vital Statistics|||||228.2|252.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|126.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||117.9|135.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|143.5|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||137.9|149.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|145.1|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|146.3|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|148.9|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||143.0|154.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|158.5|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|160.5|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||148.2|172.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|160.6|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||151.4|169.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|162.6|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|162.8|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|165.6|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||159.6|171.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|172.2|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||163.5|181.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|173.2|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|187.2|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|189.2|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C137|PA Eddie-->Vital Statistics|||||182.5|195.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|213.2|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||200.5|225.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|192.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Solely respresentative of American Indian population||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|75.4|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C141|PA Eddie-->Vital Statistics|||||56.8|94.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|94.8|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||66.7|130.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|97.8|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||80.2|119.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|98.7|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|100.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|100.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||76.3|130.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|111.9|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||87.2|141.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|119.9|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|130.8|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||108.9|152.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|133.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|137.2|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||118.1|156.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|275.2|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||170.3|420.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|161.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||128.6|201.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|164.2|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|178.7|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||157.4|200.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|179.4|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||154.3|204.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|186.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||166.1|206.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|188.7|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|196.7|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||177.6|217.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|205.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C139|PA Eddie-->Vital Statistics|||||194.9|216.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|213.8|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|221.2|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||174.0|277.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|222.9|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||197.2|248.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|230.3|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||202.8|257.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|236.4|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|239.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|83.1|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||46.6|142.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|101.9|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||87.5|116.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|106.5|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||68.3|158.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|110.6|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|111.6|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||97.5|125.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|114.0|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|116.0|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C140|PA Eddie-->Vital Statistics|||||96.2|135.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|120.6|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||92.0|155.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|132.8|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||130.0|139.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|172.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|206.4|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|97.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|102.5|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C142|PA Eddie-->Vital Statistics|||||50.6|154.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|32.5|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||11.3|53.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|68.7|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|116.3|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|166.8|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||54.5|425.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|128.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||118.4|140.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|138.1|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||117.3|158.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|151.1|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||142.1|160.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|152.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||145.9|159.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|154.2|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||147.1|161.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|154.9|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|162.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||152.8|172.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|163.2|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|164.9|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|165.7|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|170.5|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|170.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C138|PA Eddie-->Vital Statistics|||||161.8|179.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|171.6|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||161.5|182.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|215.2|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||199.9|230.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|104.2|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||95.0|113.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|114.7|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C99|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||103.9|126.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|126.1|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|128.1|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||120.9|135.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|128.6|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|130.9|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|135.9|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|136.4|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||121.3|151.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|139.3|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|147.1|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||135.4|158.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|150.2|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||139.6|161.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|150.6|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||142.8|158.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|153.1|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|163.4|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C143|PA Eddie-->Vital Statistics|||||155.3|171.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|163.6|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|184.3|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||168.8|199.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|93.0|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|86.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|152.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|93.6|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|140.6|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|143.4|Seattle, WA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||129.4|158.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|168.0|San Antonio, TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||154.0|181.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|170.0|New York City, NY|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|172.7|San Diego County, CA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.Suggested ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|180.3|Fort Worth (Tarrant County), TX|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|National Center for Health Statistics|||||170.1|190.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|181.4|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder C00-C97|||||166.4|196.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|185.8|Las Vegas (Clark County), NV|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Nevada Vital Records - Clark County Deaths|||||176.4|195.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|189.2|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|193.1|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||172.4|213.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|197.0|Boston, MA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|199.8|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|204.6|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||190.0|220.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|209.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|224.9|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|229.9|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C144|PA Eddie-->Vital Statistics|||||218.2|241.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|258.3|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||236.2|280.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|American Indian/Alaska Native|127.1|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Asian/PI|116.7|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Black|224.8|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Hispanic|133.8|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|White|194.3|U.S. Total, U.S. Total|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Health, United States, 2016, HHS/CDC/NCHS, Table 25 https://www.cdc.gov/nchs/data/hus/2016/025.pdf||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|144.0|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||140.0|148.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|154.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||142.1|165.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|155.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||146.8|164.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|168.6|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||160.1|177.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|173.2|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|175.8|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|178.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|192.8|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C145|PA Eddie-->Vital Statistics|||||186.1|199.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|198.4|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||186.2|210.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|323.0|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|103.8|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||132.2|174.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|111.4|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|116.8|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||96.8|136.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|121.2|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||94.5|153.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|126.7|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C149|PA Eddie-->Vital Statistics|||||102.8|150.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|185.2|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|153.2|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||148.3|158.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|192.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||150.8|242.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|202.5|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||178.2|226.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|204.9|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||185.6|225.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|207.4|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|213.9|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||187.8|240.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|214.5|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C147|PA Eddie-->Vital Statistics|||||203.7|225.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|241.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|245.1|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|72.9|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||43.2|115.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|111.3|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|116.9|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C148|PA Eddie-->Vital Statistics|||||97.9|135.8
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|117.9|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||90.2|151.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|130.1|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||121.2|139.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|195.9|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|43.3|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|60.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C150|PA Eddie-->Vital Statistics|||||28.8|92.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|117.4|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||64.2|197.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|174.3|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|146.5|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||125.1|168.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|153.5|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||148.3|158.7
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|155.4|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||145.8|165.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|159.6|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|160.2|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|162.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||152.7|173.0
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|165.4|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C146|PA Eddie-->Vital Statistics|||||156.6|174.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|167.7|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|202.6|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||187.8|217.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|128.4|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||123.3|133.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|130.2|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||119.2|141.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|142.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||132.3|153.4
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|145.8|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|147.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||131.4|162.5
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|148.1|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|148.5|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|163.9|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C151|PA Eddie-->Vital Statistics|||||155.8|172.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|168.4|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||153.6|183.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|165.0|Oakland (Alameda County), CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Alameda County vital statistics files|Using 2010 mid-year population estimates||||146.3|183.6
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|165.8|San Diego County, CA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||159.3|172.2
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|191.7|Portland (Multnomah County), OR|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|CDC Wonder|||||176.5|206.9
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|207.5|Indianapolis (Marion County), IN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|MCPHD Death Certificate data|||||192.9|223.1
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|211.0|Denver, CO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|220.3|Minneapolis, MN|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Minnesota Vital Statistics||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|221.1|Kansas City, MO|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|||||||
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|238.6|Philadelphia, PA|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C152|PA Eddie-->Vital Statistics|||||226.8|250.3
Cancer|All Types of Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|240.8|Columbus, OH|All cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: C00-C97|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||219.7|261.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|12.3|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|16.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|17.7|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2010-2012|||15.6|20.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|19.2|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||14.6|25.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|19.4|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|19.5|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|21.4|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|21.4|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||19.2|23.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|22.1|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|22.1|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Female breast cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|22.7|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||18.6|27.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|23.1|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.0|29.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|23.2|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|24.3|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|24.7|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||21.6|27.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|25.0|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|26.0|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|26.7|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||23.2|30.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|27.1|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||23.8|30.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|29.4|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|36.5|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||28.8|45.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|American Indian/Alaska Native|6.3|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|American Indian/Alaska Native|11.5|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Female breast cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|American Indian/Alaska Native|66.4|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|0.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|7.4|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|11.4|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||7.1|17.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|11.9|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Female breast cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|12.6|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|13.8|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2010-2012|||11.0|17.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|18.8|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||10.5|27.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|20.9|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|32.3|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||18.8|51.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI|37.5|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Asian/PI||Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|17.8|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|22.7|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|23.1|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|23.2|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||7.4|55.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|26.3|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|28.2|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|28.5|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.3|42.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|30.2|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|31.3|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2010-2012|||20.5|46.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|31.3|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Female breast cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|35.0|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|35.3|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||22.8|47.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|36.2|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|38.2|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|39.9|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|40.7|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black|42.5|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||28.7|60.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Black||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|0.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|0.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|6.9|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|7.9|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||3.2|12.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|8.7|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|8.8|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|9.2|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|13.6|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||2.8|39.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|14.4|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Female breast cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|14.7|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|15.4|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2010-2012|||9.8|23.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|16.7|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||12.3|22.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|17.1|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic|25.8|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||20.8|30.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Hispanic||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Multiracial|0.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Multiracial|100.5|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other|0.7|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other|3.8|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||0.0|11.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other|13.8|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|Other||Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|9.3|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|10.8|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|20.2|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2010-2012|||16.6|24.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|20.3|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|20.4|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|20.7|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|21.5|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.7|28.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|21.5|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|21.5|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|22.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||16.2|30.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|22.1|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Female breast cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|22.7|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|23.8|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|24.4|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||21.4|27.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|25.7|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||20.7|30.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|26.2|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|28.0|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||23.9|32.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|28.9|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|White|40.3|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||26.3|59.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|12.9|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.2|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|16.8|San Jose, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.1|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.7|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|18.6|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|19.3|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|20.9|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.7|23.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|21.0|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|21.5|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|21.6|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|21.8|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||17.3|27.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|22.1|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|22.3|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|22.8|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|23.5|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|23.7|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|24.3|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||21.2|27.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|25.2|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||20.9|30.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|25.5|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|26.2|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|28.9|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||23.2|35.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|30.5|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||26.9|34.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|30.6|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|30.9|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|American Indian/Alaska Native|0.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|American Indian/Alaska Native|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|American Indian/Alaska Native|0.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|American Indian/Alaska Native|10.8|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|2.9|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|4.5|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|7.1|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|9.7|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|11.2|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|11.3|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|11.3|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|12.2|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||8.0|17.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|14.5|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|15.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|18.8|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI|26.7|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||16.0|37.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Asian/PI||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|15.6|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|18.3|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|20.1|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|20.3|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|26.6|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|26.7|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|29.1|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|29.7|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|30.9|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|31.2|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|31.8|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|33.7|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|33.8|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|35.4|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|36.0|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|36.5|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|40.4|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||26.4|54.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|41.7|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||29.1|58.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black|47.3|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Black||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|3.1|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|8.7|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|10.5|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|11.2|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||6.0|16.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|11.3|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|12.2|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|12.5|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|14.1|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|15.5|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|15.9|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|15.9|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||11.9|20.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|16.1|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|16.6|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|17.4|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic|28.4|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Hispanic||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Multiracial|0.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Multiracial|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Multiracial|7.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other|0.6|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other|5.3|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||0.0|15.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other|7.1|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other|8.8|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other|272.4|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|9.0|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|13.5|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|13.8|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|16.0|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|17.9|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|18.0|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|20.3|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|21.0|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|21.4|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|21.6|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|21.8|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|21.8|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|22.9|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||18.0|28.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|23.1|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||20.1|26.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|24.1|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|24.1|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.0|31.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|24.2|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|24.4|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|25.4|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|25.4|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||21.5|29.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|White|29.3|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.1|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.3|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|15.1|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|17.7|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.0|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||14.0|22.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.8|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|19.0|San Jose, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|19.2|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|19.9|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|20.4|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.6|26.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|21.1|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|21.3|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|21.5|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||17.6|26.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.2|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.2|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||19.2|25.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.2|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||19.9|24.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.3|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|24.8|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|25.3|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||19.1|32.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|25.8|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||22.5|29.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|26.2|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|26.7|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|27.2|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|27.8|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|30.8|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|American Indian/Alaska Native|0.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|American Indian/Alaska Native|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|American Indian/Alaska Native|4.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|American Indian/Alaska Native|10.8|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|3.5|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|7.6|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|9.2|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|10.3|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|10.3|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|10.8|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|11.3|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|16.5|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||8.6|24.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|19.3|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI|38.1|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Asian/PI||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|11.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|15.4|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|17.4|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|21.2|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|23.0|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|26.0|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||15.2|36.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|29.0|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|29.5|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|30.2|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|30.2|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|30.7|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|31.8|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|34.5|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||21.4|52.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|35.1|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|36.6|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|38.0|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|38.9|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|41.1|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||27.7|58.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black|41.6|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Black||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|5.4|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|6.1|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|6.1|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|7.1|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|8.1|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|13.2|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|14.7|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||11.0|19.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|14.7|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|15.2|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|17.4|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|18.3|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||10.7|26.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|18.4|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic|22.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Hispanic||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Multiracial|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Multiracial|3.5|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Multiracial|26.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Consider omission due to wide confidence intervals and underreporting of this category.  Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Other|3.7|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||0.0|11.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Other|8.9|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Other|10.8|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Other||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Other||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|8.6|Los Angeles, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|11.8|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|11.9|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|13.7|Houston, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|17.4|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|17.5|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||13.3|22.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|19.3|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|20.1|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|20.7|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|21.3|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|21.7|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.7|29.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|22.3|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|22.5|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|23.5|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|23.9|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||20.1|27.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|24.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|25.4|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|25.9|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|26.0|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||15.1|41.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|26.7|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||23.5|30.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|White|27.9|Washington, DC|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|16.8|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|17.1|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|17.5|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|17.7|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2013-2015|||15.6|20.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.4|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.6|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.7|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||15.1|23.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.9|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||14.9|23.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|19.8|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.8|21.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|20.2|San Jose, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|20.3|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||17.4|23.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|20.5|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|20.6|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|20.8|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|21.2|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|21.2|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|24.9|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|25.5|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||22.3|28.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|25.7|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|26.6|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||23.3|29.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|27.1|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.5|33.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|28.6|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|31.1|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||24.3|39.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|American Indian/Alaska Native|0.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|American Indian/Alaska Native|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|American Indian/Alaska Native|0.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|American Indian/Alaska Native|10.1|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|American Indian/Alaska Native|55.4|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|American Indian alone||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|5.8|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|6.4|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|10.0|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|11.1|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|11.6|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|13.8|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|14.8|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||7.3|22.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|14.9|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2013-2015|||12.0|18.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|20.0|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI|24.7|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||13.1|42.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Asian/PI||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|18.2|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|21.3|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||13.9|31.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|22.4|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|24.8|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2013-2015|||15.6|38.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|25.7|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|25.9|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|26.2|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|28.5|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|28.7|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|29.2|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|29.2|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|30.1|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||18.5|41.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|32.3|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|32.4|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.3|47.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|33.3|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|33.6|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black|44.7|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||30.8|62.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Black||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|5.5|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|5.8|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|12.3|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||7.0|17.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|13.8|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|14.4|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|14.6|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|14.6|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|14.7|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2013-2015|||9.2|22.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|16.2|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|16.9|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||10.2|26.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|17.8|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||13.7|22.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|20.8|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic|49.3|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Hispanic||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Multiracial|0.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Multiracial|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Multiracial|4.0|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other|10.0|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other|19.7|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||0.4|39.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|13.7|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|15.8|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|17.3|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Female Breast Cancer Mortality Rate ICD-10 codes: C50, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|18.9|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||14.7|24.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|19.9|San Francisco, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||Value is reported for a multi-year period, 2013-2015|||16.3|24.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|20.7|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|20.9|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|21.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|21.1|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|21.2|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||17.6|24.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|22.7|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||19.8|25.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|22.7|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|23.1|Chicago, Il|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|24.5|Phoenix, AZ|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|24.9|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|26.1|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||19.6|34.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|27.1|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Colorado Vital Records Death Data|Filtered to female gender only since the category is called Female Breast Cancer. 2011-2013 years are the most recently available data at this time.|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|27.6|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||17.1|42.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|White|27.9|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||23.8|32.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|16.8|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|18.1|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||14.7|24.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.8|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||16.1|24.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.8|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.9|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics|Breast cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C50||||14.5|26.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.9|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.8|22.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|20.1|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||16.0|24.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|20.6|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||17.6|23.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|20.6|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2014, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|20.7|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|20.7|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|21.1|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|24.7|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||21.5|27.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|25.3|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.0|31.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|25.6|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||22.4|28.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|26.1|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||22.8|29.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|26.6|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C50||||21.9|31.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|30.2|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|American Indian/Alaska Native|0.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|American Indian/Alaska Native|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|American Indian/Alaska Native|10.8|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2014, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|0.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|7.0|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|11.6|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2014, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|12.2|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|16.9|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||12.1|23.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|19.8|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||11.5|28.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI|40.5|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Asian/PI||Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|11.2|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|13.7|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|24.9|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|25.9|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||17.5|37.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|27.3|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|28.2|Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C50||||23.0|33.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|28.3|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.3|41.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|28.8|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2014, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|30.8|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics|Breast cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C50||||19.3|46.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|32.2|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|34.7|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||22.3|47.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|39.5|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||25.6|58.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black|42.4|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Black||Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|0.0|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|10.5|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|13.1|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||7.0|19.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|13.6|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||10.2|17.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|14.5|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2014, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|15.3|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|16.1|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|17.6|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic|20.4|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||16.5|25.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic||Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C50|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Hispanic||Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Multiracial|35.2|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Consider omission due to wide confidence intervals and underreporting of this category.  Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Multiracial|100.5|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other|0.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other|3.8|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||0.0|11.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|Other||Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|12.1|Long Beach, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|16.7|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|19.3|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder: C50|||||15.0|24.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|20.6|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nat'l Vital Statistics Report, Final Deaths Data 2014, Tables 16-17, C50, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|21.7|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.9|24.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|22.0|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|22.3|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||18.6|26.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|22.6|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.6|29.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|22.9|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|23.3|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|23.6|Miami (Miami-Dade County), FL|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|23.9|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||18.1|32.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|24.9|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|26.9|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||21.7|32.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|27.7|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||23.6|31.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White|28.0|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County Vital Statistics|Breast cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C50||||16.8|43.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|White||Detroit, MI|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C50|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|16.7|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|18.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||13.8|23.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|18.9|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|19.0|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.0|20.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|19.8|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||16.1|24.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|20.0|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|20.3|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder C50|||||16.3|25.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|20.3|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Health, United States, 2016, HHS/CDC/NCHS, Table 26 https://www.cdc.gov/nchs/data/hus/2016/026.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|21.3|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||18.4|24.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|22.5|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||19.6|25.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|24.3|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||21.2|27.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|24.4|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||21.2|27.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|24.6|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|24.9|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|28.5|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.7|35.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|32.5|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||25.6|40.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|12.8|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Health, United States, 2016, HHS/CDC/NCHS, Table 26 https://www.cdc.gov/nchs/data/hus/2016/026.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder C50||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|0.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|9.5|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||3.9|15.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|9.6|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|11.7|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Health, United States, 2016, HHS/CDC/NCHS, Table 26 https://www.cdc.gov/nchs/data/hus/2016/026.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|14.4|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||10.1|19.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|20.3|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. Suppressed to protect confidentiality |||10.3|40.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|28.7|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||17.0|45.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder C50||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|17.7|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Suppressed to protect confidentiality|||5.5|44.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|26.4|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|27.2|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||16.6|42.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|27.7|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Health, United States, 2016, HHS/CDC/NCHS, Table 26 https://www.cdc.gov/nchs/data/hus/2016/026.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|29.5|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|30.1|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|30.3|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|31.6|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.6|46.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|34.9|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||22.8|47.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|37.9|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||27.9|50.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|50.6|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||35.8|69.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder C50||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|0.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|13.5|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Health, United States, 2016, HHS/CDC/NCHS, Table 26 https://www.cdc.gov/nchs/data/hus/2016/026.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|14.3|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||10.8|18.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|15.4|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|15.4|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|19.2|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||11.7|26.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|20.4|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||16.4|24.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder C50||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other|0.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other|0.0|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other|5.3|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||0.0|15.6
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other||Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other||Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other||San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|16.1|Boston, MA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|16.1|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|18.8|Seattle, WA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||13.7|26.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|18.9|Las Vegas (Clark County), NV|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Nevada Vital Records - Clark County Deaths|||||15.5|22.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|20.4|U.S. Total, U.S. Total|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Health, United States, 2016, HHS/CDC/NCHS, Table 26 https://www.cdc.gov/nchs/data/hus/2016/026.pdf||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|20.5|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder C50|||||16.1|25.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|20.5|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.8|23.2
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|21.0|Fort Worth (Tarrant County), TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|National Center for Health Statistics|||||17.4|24.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|22.3|New York City, NY|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|22.6|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|27.3|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||15.9|43.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|27.8|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.9|36.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|30.1|San Antonio, TX|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Bexar County level data|||24.6|35.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|10.2|Minneapolis, MN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Minnesota Vital Statistics||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|19.1|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder|||||15.1|23.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|19.8|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.8|21.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|20.4|Indianapolis (Marion County), IN|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|MCPHD Death Certificate data|||||16.6|24.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|21.3|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|23.8|Philadelphia, PA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|PA Eddie-->Vital Statistics|||||20.7|26.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|24.5|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|25.7|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.3|32.1
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|32.5|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||25.8|40.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Asian/PI|11.9|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Asian/PI|13.3|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||9.2|18.4
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Asian/PI|29.1|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||17.5|45.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Asian/PI||Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Asian/PI||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Black|23.1|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Black|25.1|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.7|38.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Black|29.9|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.5|45.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Black|40.8|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||27.9|57.7
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Black|56.6|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Black||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Hispanic|0.0|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Hispanic|15.6|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||12.1|19.9
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Hispanic|23.7|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Hispanic||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Hispanic||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Other|0.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Other||Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Other||Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|Other||San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|White|19.3|Portland (Multnomah County), OR|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|CDC Wonder|||||15.0|24.5
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|White|21.0|Denver, CO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|White|21.5|Kansas City, MO|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|||||||
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|White|21.5|San Diego County, CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.7|24.3
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|White|27.5|Columbus, OH|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.7|35.8
Cancer|Female Breast Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|White|31.8|Oakland (Alameda County), CA|Breast cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C50|Alameda County vital statistics files|Using 2010 mid-year population estimates||||20.2|47.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|18.7|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|19.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||16.3|23.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|25.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|31.4|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|35.0|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||32.8|37.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|36.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||34.5|39.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|37.2|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||32.2|42.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|38.4|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|41.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||34.7|47.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|41.7|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|42.1|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|44.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|46.6|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||43.4|49.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|46.7|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|47.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|48.6|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|52.1|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|54.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics|||||51.2|58.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|58.0|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|58.2|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||53.1|63.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|64.0|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||57.1|71.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|0.0|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|33.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|316.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|15.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|17.1|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|19.6|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||11.6|33.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|24.8|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|25.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|27.1|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics|||||13.4|40.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|27.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|27.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||22.0|34.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|27.8|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||19.8|35.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|28.1|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|33.2|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||29.9|36.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|37.6|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||25.9|52.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|46.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|20.5|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|35.4|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|39.2|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||27.6|54.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|41.9|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|45.2|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||34.7|55.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|46.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||34.6|61.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|47.5|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|48.9|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||30.2|76.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|49.4|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|52.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|54.5|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|55.7|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|56.2|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||45.9|68.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|56.4|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|58.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|58.7|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||45.9|73.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|63.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics|||||57.7|70.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|64.1|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|64.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||52.2|79.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|65.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||54.0|79.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|71.1|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|12.5|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|14.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics|||||6.7|22.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|15.6|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||10.5|20.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|17.0|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|19.6|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|19.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||16.3|23.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|20.4|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|20.7|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||15.5|27.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|21.2|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||17.0|25.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|21.6|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|23.6|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|27.1|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|35.4|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|49.8|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|0.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|0.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|0.3|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|24.6|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|25.9|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.8|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|23.7|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|35.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||25.4|47.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|35.9|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||32.5|39.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|39.3|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||33.3|46.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|39.8|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|41.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||38.2|43.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|43.9|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|46.8|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|46.8|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||41.7|52.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|47.6|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|49.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|50.8|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|51.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|51.4|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|51.5|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|54.3|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics|||||49.1|59.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|54.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||50.8|59.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|57.9|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||52.0|64.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|62.0|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|66.7|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||58.1|75.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|12.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||9.4|16.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|15.2|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|19.9|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|26.7|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|27.3|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||24.7|30.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|29.6|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||23.7|36.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|30.4|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|30.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||28.0|33.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|31.7|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|31.8|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|31.8|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|38.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|39.1|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|40.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|40.4|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|40.8|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||36.7|44.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|46.3|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics||||||39.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|48.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||42.5|55.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|52.0|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|54.7|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||46.3|63.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|23.3|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|23.8|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|29.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||23.4|37.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|44.4|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||40.8|48.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|44.5|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2010-2012|||40.8|48.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|46.6|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|46.9|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||37.1|58.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|47.9|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||39.5|57.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|50.2|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|53.5|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||48.6|58.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|55.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|56.2|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|57.5|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|60.3|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Lung cancer deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|60.6|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|61.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|65.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics||||||56.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|67.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|71.3|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||62.6|81.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|74.4|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|76.0|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||64.2|87.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|16.6|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|25.0|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|29.4|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|31.6|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|33.8|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|34.2|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|34.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|34.6|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|35.1|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|36.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|36.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|36.3|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||34.2|38.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|39.0|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|40.2|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|42.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|46.0|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|47.3|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|47.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|49.9|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||44.3|55.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|51.8|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||48.5|55.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|53.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|56.5|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|58.3|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||54.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|60.3|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||55.1|65.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|62.3|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||55.4|69.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|26.8|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|30.0|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|190.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|17.1|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|19.0|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|20.7|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|23.0|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|24.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|24.3|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|24.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|25.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|28.4|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||22.9|34.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|28.8|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|30.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||21.3|38.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|32.6|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|34.7|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|42.4|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||27.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|49.2|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|23.5|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|24.0|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|29.2|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|38.1|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|44.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||32.9|59.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|46.8|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|47.9|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|48.7|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|48.9|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|49.8|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|50.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|51.8|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|54.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|56.7|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||44.5|68.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|56.9|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|57.5|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|58.1|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|59.3|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|61.5|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|62.8|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|63.5|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||50.9|78.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|63.6|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||39.8|96.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|64.1|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||58.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|74.2|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||61.9|88.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.7|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|11.8|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|12.3|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||5.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|13.8|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|14.3|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|17.2|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|17.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|17.5|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|19.6|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|19.7|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|19.8|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|19.9|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|20.6|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|21.4|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|23.8|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||19.4|28.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|24.2|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|26.6|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|27.2|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||19.4|35.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|30.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|5.6|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|6.2|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|21.4|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||4.3|38.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|25.7|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|36.0|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|10.3|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|27.5|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|28.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|33.5|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|34.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|37.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|37.6|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|38.0|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|40.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||37.3|42.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|40.6|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|41.9|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|42.0|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|42.8|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|43.3|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|47.8|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|48.6|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|49.2|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|51.4|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|51.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||45.5|57.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|51.5|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|53.8|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|55.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||50.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|58.4|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||52.5|64.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|59.1|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||54.8|63.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|64.3|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||55.9|72.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.3|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|19.2|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|24.2|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|24.3|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|26.3|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|26.8|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|27.0|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|27.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|28.6|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|28.9|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|29.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|29.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||27.3|32.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|30.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|31.7|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|34.3|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|34.5|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|36.5|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|37.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|44.7|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||40.5|49.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|44.7|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||37.8|51.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|45.7|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|46.3|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||40.4|53.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|49.6|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||41.5|57.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|49.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics||||||45.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|51.4|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|20.9|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|25.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|41.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|43.0|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|43.2|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|43.5|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|44.3|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|44.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||41.2|48.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|45.4|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|45.6|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|46.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|48.6|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|48.7|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|53.3|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|55.7|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||47.1|64.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|57.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|58.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|60.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||55.5|66.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|62.6|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|65.8|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|71.5|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics||||||64.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|73.0|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|77.3|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|80.2|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||70.9|90.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|80.6|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||68.3|92.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|15.8|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|28.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|29.0|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|29.3|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|31.8|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|32.0|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|33.8|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|34.0|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|34.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||32.8|37.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|39.2|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|39.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|40.2|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|40.7|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||35.8|45.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|44.5|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|44.9|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C34||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|45.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|46.2|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|46.2|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|51.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||47.6|54.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|55.3|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|57.0|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||53.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|61.2|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|62.2|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||55.3|69.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|62.5|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||57.2|68.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|85.2|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||70.9|99.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|7.1|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|30.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C37||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|97.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|14.8|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|15.8|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|19.9|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|23.3|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|24.0|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C38||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|25.7|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|26.2|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||17.0|38.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|26.3|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||14.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|26.5|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||21.4|32.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|26.7|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||18.7|34.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|27.4|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|27.4|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|30.1|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|33.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|42.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|48.0|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||29.7|73.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|29.3|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|32.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|33.1|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|34.8|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.7|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.8|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|46.8|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|47.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||35.5|62.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|48.1|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|49.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C39||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|50.3|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|52.1|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|52.3|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|52.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|53.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|55.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||43.3|69.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|56.0|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|58.4|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||46.7|72.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|61.4|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||48.7|74.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|62.1|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|62.7|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|62.8|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||56.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|78.8|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||66.4|93.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.9|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|9.0|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|14.5|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|17.0|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|18.1|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|18.2|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|18.5|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||14.8|22.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|19.2|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C40||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|19.4|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|20.0|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|21.3|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|21.8|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|22.2|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|27.2|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||16.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|27.3|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|28.1|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||20.6|35.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|32.9|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|7.8|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.6|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|4.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||0.0|9.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|20.4|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|30.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.7|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|21.6|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|30.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||21.4|41.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|33.6|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|33.6|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|36.6|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|37.1|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|38.3|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|39.4|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||36.7|42.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|39.7|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||34.6|44.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|40.6|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|40.7|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|41.4|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|43.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|45.3|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|47.3|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|48.0|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C41||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|50.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|52.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||47.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|54.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|56.0|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|56.9|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|57.3|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||53.1|61.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|60.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||54.6|67.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|65.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||57.3|74.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.7|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.3|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.2|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|24.2|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|24.8|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|26.1|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|29.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|30.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||28.3|33.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|32.1|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|32.2|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|32.8|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|34.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|36.4|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C35||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|37.4|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|37.4|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|37.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|39.2|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||32.9|45.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|41.7|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|45.7|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||41.4|50.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|46.4|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics||||||42.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|48.9|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|49.9|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||43.8|56.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|50.5|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||42.3|58.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|50.7|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.1|Los Angeles, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|27.0|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|35.8|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|38.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|40.1|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||36.7|43.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|40.4|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|42.6|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||35.1|50.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|43.3|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|44.0|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|44.6|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|45.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|48.6|Houston, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|50.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|51.9|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||41.6|63.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|51.9|Washington, DC|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|53.5|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|56.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2012, Tables 16-17, C33-C36||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|57.8|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||52.6|63.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|59.1|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|59.3|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|73.4|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics||||||66.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|76.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|79.2|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|79.4|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||67.2|91.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|80.5|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||71.2|90.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|26.9|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|28.1|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|28.8|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|29.5|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|30.9|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|31.3|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|33.2|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|33.5|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||31.3|35.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|34.4|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||32.3|36.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|34.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|37.9|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||31.8|44.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|39.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||36.1|42.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|41.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|42.0|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|42.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||37.5|47.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|43.4|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|44.9|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|49.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||46.6|53.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|55.5|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||51.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|59.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||54.0|64.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|59.9|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|60.1|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|62.3|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||55.5|69.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|4.5|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|16.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|27.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|138.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|11.3|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|15.2|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|21.2|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|23.3|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|25.1|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|25.3|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|28.1|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||15.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|28.4|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|30.1|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||24.4|35.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|36.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||25.3|50.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|36.5|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||33.1|40.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|36.8|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|37.3|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||27.3|47.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|27.2|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|29.8|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|33.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|34.8|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|37.1|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|41.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||31.0|55.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|44.3|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|44.4|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|46.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||36.4|58.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|46.7|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|47.1|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|48.2|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|50.8|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||38.8|62.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|54.3|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||44.3|66.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|54.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||42.3|68.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|55.9|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||45.6|67.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|56.8|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|58.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||53.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|60.9|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||48.5|75.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|62.6|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|63.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.5|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|12.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||7.3|19.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|15.1|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|18.8|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|19.1|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||15.3|22.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|19.6|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||14.6|25.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|20.7|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|20.7|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|21.3|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|24.1|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||16.6|31.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|25.5|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|30.4|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|31.6|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||20.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|45.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|12.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|7.6|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||0.0|18.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|19.4|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|28.8|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||20.6|39.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|29.2|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||26.1|32.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|31.8|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|34.9|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|35.2|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|35.8|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|39.1|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||36.4|41.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|39.2|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|39.8|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|40.7|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|42.4|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|43.3|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|43.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||38.0|48.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|43.7|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||39.8|47.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|46.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|46.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|47.2|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|51.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|52.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||48.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|56.4|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||52.3|60.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|58.9|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|62.9|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||56.8|69.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|64.7|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||56.3|73.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.2|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|23.1|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|23.9|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|25.3|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||22.8|28.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|25.5|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|26.1|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|26.4|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|26.7|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|28.3|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||25.8|30.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|31.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|31.9|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||28.1|35.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|32.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|32.7|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|35.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|37.9|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||31.8|44.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|38.0|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|42.2|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||38.1|46.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|44.9|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|45.2|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics||||||40.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|48.1|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||42.2|54.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|50.1|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|52.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||44.5|61.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|27.9|Phoenix, AZ|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|32.8|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|37.6|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Lung Cancer Mortality Rate ICD-10 codes: C33-C34, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|37.7|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|38.7|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|40.2|San Jose, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|42.3|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||38.9|45.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|43.1|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|43.1|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|44.5|San Francisco, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Value is reported for a multi-year period, 2013-2015|||40.8|48.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|47.1|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||37.5|58.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|49.3|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||41.0|57.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|49.9|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||44.3|55.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|53.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths Data 2013, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|54.4|Chicago, Il|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|54.5|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|55.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|59.2|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||53.9|64.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|71.0|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics||||||64.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|73.3|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||61.8|84.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|75.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||66.1|84.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|75.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|81.7|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|19.6|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||16.4|22.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|27.9|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||23.8|32.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|28.4|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|29.9|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|30.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||28.1|31.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|34.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|34.7|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34||||28.8|40.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|35.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||31.0|39.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|38.0|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||35.0|41.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|41.5|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|42.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|46.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|52.0|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C33-34||||47.0|57.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|53.8|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||50.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|54.3|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||50.8|57.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|56.9|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||52.0|62.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|59.3|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|60.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||54.0|67.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|27.8|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|49.3|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|17.9|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|21.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|22.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|22.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|22.9|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||14.6|35.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|24.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||20.0|29.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|25.5|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|26.0|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||14.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|38.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Represents Asiain population alone. Does not include Pacific Islander population|||27.3|52.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|40.8|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|43.6|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||33.1|54.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|24.7|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|30.0|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|30.7|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|32.6|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||17.8|55.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|34.2|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|36.5|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||26.2|49.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|42.7|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||33.4|53.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|43.1|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34||||32.1|56.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|45.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|46.5|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||34.9|60.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|53.2|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C33-34||||47.8|58.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|54.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||43.2|66.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|55.5|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||49.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|57.2|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|63.5|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||52.7|76.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|65.1|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||52.4|80.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|68.1|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|71.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|0.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|15.4|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||9.6|23.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|16.7|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||13.5|20.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|18.1|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|18.3|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|19.6|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||16.4|22.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|20.4|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||14.0|26.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|20.4|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|23.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|26.7|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|31.6|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34||||16.3|55.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|58.0|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||17.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C33-34|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|0.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|0.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|11.2|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|25.8|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34||||17.7|36.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|28.6|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||23.7|34.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|33.3|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|33.6|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||31.2|36.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|34.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|35.4|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|36.3|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||31.5|41.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|36.6|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|42.0|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C33-34||||29.4|54.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|42.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||38.5|46.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|43.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||38.9|48.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|44.8|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|45.4|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|52.1|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|53.3|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||48.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|56.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|58.3|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||52.5|64.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|61.6|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||57.2|66.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|62.4|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||54.2|70.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|13.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||10.4|17.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|21.0|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|24.0|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|26.0|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34||||19.8|33.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|26.8|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||24.4|29.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|27.1|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|27.1|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||21.7|33.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|30.2|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||26.6|33.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|30.8|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||25.3|36.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|31.9|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|32.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|32.8|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|34.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|43.5|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C33-34||||37.5|49.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|44.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics||||||40.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|46.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||40.8|53.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|48.1|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||43.6|52.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|49.4|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||41.3|57.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|49.5|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|27.6|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||21.9|34.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|29.7|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||23.4|37.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|33.6|Long Beach, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|34.3|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||31.3|37.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|37.5|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|38.4|Miami (Miami-Dade County), FL|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|39.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|41.5|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder: C33-C34|||||34.1|48.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|47.1|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County Vital Statistics|Lung cancer mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34||||37.2|58.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|49.0|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||43.6|54.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|51.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, C33-C34, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|53.3|Boston, MA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|62.1|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||56.6|67.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|62.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|65.5|Detroit, MI|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|2014 Michigan Resident Death File, Michigan Department of Health and Human Services, Division for Vital Records and Health Statistics|Age-adjusted rates are computed by the direct method, and are age-adjusted to the 2000 U.S. standard population. Detroit population for 2010 was used for 2014. Rates are per 100,000 population in the specified group. ICD-10 Codes C33-34||||56.8|74.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|67.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics||||||61.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|71.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||63.0|81.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|71.7|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|75.6|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||63.9|87.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|17.8|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||14.8|20.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|28.4|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||24.3|33.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|28.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||27.1|30.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|29.8|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|31.1|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|37.2|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||34.2|40.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|37.7|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||31.7|43.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|39.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34|||||34.8|44.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|40.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|40.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|43.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||40.8|47.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|48.8|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||44.2|53.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|51.1|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||47.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|52.2|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|64.0|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||57.1|71.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|47.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|17.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||8.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|22.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||17.8|26.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|23.3|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||15.1|35.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|23.8|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|27.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|28.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|34.5|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||20.8|53.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|37.3|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||26.7|50.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|39.5|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||29.5|49.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|31.8|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|35.1|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||25.1|47.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|36.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||26.3|48.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|38.0|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||22.7|60.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|40.2|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||30.0|50.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|41.1|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||32.2|51.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|43.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|46.8|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||37.6|57.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|53.1|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||47.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|58.8|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||46.3|73.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|60.7|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||48.4|75.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|63.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|70.3|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|10.0|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||5.9|15.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|15.9|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|17.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||13.7|20.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|18.2|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|19.0|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||17.2|20.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|21.7|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.8|63.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|23.9|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|25.3|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||17.7|32.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|29.0|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||18.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|0.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|0.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|27.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||19.0|38.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|28.4|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||23.6|34.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|32.2|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|32.8|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||30.4|35.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|35.4|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||31.1|39.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|36.5|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|38.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|40.7|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34|||||35.7|45.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|41.5|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||37.8|45.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|41.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||38.3|45.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|45.0|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|49.2|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||44.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|52.5|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||47.0|58.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|68.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||60.2|77.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|11.7|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.8|15.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|23.9|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|24.0|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||19.2|30.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|24.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|25.3|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||23.0|27.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|30.2|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||26.7|33.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|33.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|33.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|37.6|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34|||||31.6|43.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|41.0|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||36.9|45.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|44.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||38.8|51.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|45.8|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics||||||41.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|48.8|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|51.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||43.6|60.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|22.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|16.8|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|30.7|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|12.9|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|37.3|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|26.1|San Antonio, TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||20.8|32.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|33.5|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||30.5|36.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|34.4|Seattle, WA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||27.7|42.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|38.1|New York City, NY|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|NYC DOHMH Bureau of Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|39.8|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|41.0|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder C33-C34|||||34.0|48.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|44.0|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||34.9|54.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|47.3|Fort Worth (Tarrant County), TX|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|National Center for Health Statistics|||||42.0|52.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|47.9|Las Vegas (Clark County), NV|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Nevada Vital Records - Clark County Deaths|||||43.1|52.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|49.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|49.7|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|54.7|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||47.4|62.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|58.6|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics||||||52.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|64.0|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|81.3|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||69.1|93.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|American Indian/Alaska Native|32.1|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Asian/PI|29.5|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Black|58.6|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Hispanic|24.3|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|White|52.2|U.S. Total, U.S. Total|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Health, United States, 2016, HHS/CDC/NCHS, Table 24 https://www.cdc.gov/nchs/data/hus/2016/024.pdf||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|27.9|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||26.2|29.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|33.8|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|34.9|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||29.3|40.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|38.1|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder|||||33.6|42.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|40.2|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|50.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||45.4|55.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|50.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||47.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|51.7|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|57.1|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||50.5|63.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|128.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|26.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||21.2|30.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|30.0|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||20.9|41.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|30.6|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||18.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|37.1|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|38.8|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder|||||24.0|59.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|54.9|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|30.1|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||21.8|40.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|39.7|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|48.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder|||||28.7|76.5
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|51.8|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||42.3|63.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|53.5|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||41.7|67.6
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|53.7|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|54.7|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||49.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|55.8|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||44.0|69.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|56.1|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|15.2|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||12.2|18.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|19.9|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||12.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|31.7|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|20.0|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|0.0|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|7.4|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|26.8|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||18.8|37.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|31.3|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||29.0|33.7
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|33.5|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|37.1|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder|||||32.4|41.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|37.8|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|47.5|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|PA Eddie-->Vital Statistics||||||42.8
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|49.4|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|52.0|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||46.5|58.1
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|60.1|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||52.0|68.3
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|25.6|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||23.4|27.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|32.0|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|34.1|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|35.4|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder|||||29.6|41.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|42.8|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C36|PA Eddie-->Vital Statistics|||||38.7|46.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|43.7|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||36.1|51.4
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|44.6|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||38.9|51.0
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|48.2|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|31.0|San Diego County, CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 11, 2018 5:24:40 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||28.2|33.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|37.4|Oakland (Alameda County), CA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Alameda County vital statistics files|Using 2010 mid-year population estimates||||29.3|47.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|37.6|Denver, CO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from the Colorado Department of Public Health and Environment||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|42.1|Portland (Multnomah County), OR|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|CDC Wonder|||||35.1|49.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|48.4|Minneapolis, MN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Minnesota Vital Statistics||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|57.4|Indianapolis (Marion County), IN|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|MCPHD Death Certificate data|||||49.8|65.9
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|60.5|Kansas City, MO|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|||||||
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|63.2|Philadelphia, PA|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C35|PA Eddie-->Vital Statistics|||||57.1|69.2
Cancer|Lung Cancer Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|77.1|Columbus, OH|Lung cancer mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: C33-C34|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||65.0|89.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|All|95.0|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|All|99.4|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||98.1|100.6
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|All|109.0|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||107.5|110.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|All|113.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Asian/PI|12.9|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||10.8|15.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Asian/PI|29.0|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Asian/PI|47.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||44.0|50.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Black|204.0|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Black|226.9|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||223.6|230.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Black|242.2|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Black|285.6|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||278.2|293.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Hispanic|40.8|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Hispanic|48.9|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||47.3|50.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Hispanic|67.8|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||65.8|69.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Hispanic|140.8|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Other|10.7|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|Other|102.8|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||95.4|110.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|White|23.6|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||22.4|24.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|White|37.4|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|White|44.9|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Both|White|110.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||108.5|112.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Female|All|94.8|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||93.1|96.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Female|All|105.1|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Female|All|114.0|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Female|All|131.5|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||129.2|133.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Male|All|86.9|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||85.1|88.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Male|All|103.5|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||101.7|105.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Male|All|109.1|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2010|Male|All|111.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|All|30.5|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|All|57.1|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|All|92.1|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|All|104.7|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|All|108.3|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||107.0|109.6
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|All|111.5|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||110.0|113.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|American Indian/Alaska Native|45.4|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||American Indian alone||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Asian/PI|12.9|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||10.8|15.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Asian/PI|15.9|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Asian/PI|18.9|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Asian/PI|30.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Asian/PI|47.9|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||44.6|51.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Black|104.7|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Black|163.6|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Black|199.2|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Black|223.2|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Black|238.8|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||235.5|242.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Black|298.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||290.8|306.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Hispanic|30.7|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Hispanic|38.6|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Hispanic|60.6|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||58.8|62.4
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Hispanic|62.8|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Hispanic|68.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||66.6|70.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Hispanic|128.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Other|3.2|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Other|6.8|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Other|44.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|Other|112.6|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||104.9|120.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|White|22.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|White|24.9|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||23.6|26.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|White|33.7|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|White|40.3|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|White|42.4|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Both|White|110.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||108.5|112.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Female|All|31.0|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Female|All|54.6|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Female|All|101.5|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||99.7|103.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Female|All|103.5|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Female|All|106.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Female|All|134.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||132.1|136.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Male|All|29.9|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Male|All|59.1|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Male|All|88.9|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||87.0|90.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Male|All|101.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Male|All|104.7|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2011|Male|All|114.8|Chicago, Il|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||113.0|116.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|31.9|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|40.1|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|47.2|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|63.9|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|65.7|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|90.6|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|99.0|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||95.9|102.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|99.2|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|105.4|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|116.0|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|119.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||118.2|121.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|All|128.4|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|American Indian/Alaska Native|56.8|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||American Indian alone||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Asian/PI|17.5|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Asian/PI|17.8|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Asian/PI|18.5|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Asian/PI|24.7|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||20.9|28.6
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Asian/PI|28.1|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Asian/PI|52.3|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||48.8|55.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|119.6|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|120.1|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|126.5|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|194.4|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|208.3|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|232.1|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|271.2|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||260.9|281.6
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Black|350.6|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||342.4|358.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|31.6|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|45.2|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|46.5|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||42.1|50.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|47.4|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|54.8|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|72.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||70.5|74.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|75.2|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Hispanic|119.9|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Other|3.7|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Other|7.1|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Other|52.0|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|Other|138.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||129.9|147.4
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|23.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|26.3|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|33.2|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||29.3|37.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|35.3|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|36.3|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|40.0|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|44.9|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Both|White|112.8|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||110.5|115.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|32.1|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|37.9|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|44.6|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|61.7|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|61.9|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|63.0|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|88.2|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|97.5|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|108.3|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||103.8|112.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|115.9|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|126.3|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Female|All|142.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||140.3|145.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|31.8|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|42.9|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|49.4|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|52.5|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|64.2|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|69.1|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|89.1|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2012 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||84.9|93.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|93.0|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|97.1|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||95.2|99.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|100.0|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|122.3|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2012|Male|All|129.2|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|31.7|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|46.2|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|46.4|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|58.6|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|59.5|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|85.3|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|91.6|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|103.0|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||99.9|106.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|104.7|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|114.8|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|119.5|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||118.0|121.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|All|125.6|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|American Indian/Alaska Native|53.3|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||American Indian alone||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Asian/PI|16.4|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Asian/PI|17.2|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Asian/PI|19.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Asian/PI|26.5|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||22.4|30.6
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Asian/PI|30.7|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Asian/PI|48.3|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||44.9|51.6
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|110.7|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|112.3|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|118.1|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|169.2|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|198.0|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|219.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|265.5|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||255.4|275.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Black|356.1|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||347.8|364.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|32.7|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|41.7|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|48.9|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|50.6|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|61.3|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||59.3|63.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|63.6|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||58.1|69.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|69.5|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Hispanic|127.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Other|3.4|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Other|55.8|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Other|239.8|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||228.6|250.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|Other||Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|22.8|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|25.8|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|31.3|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||27.6|34.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|31.9|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|32.6|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|37.6|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|41.1|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Both|White|110.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||108.2|112.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|33.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|44.8|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|44.9|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|56.2|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|58.3|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|59.4|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|84.2|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|98.8|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|103.8|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|109.2|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||104.6|113.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|123.4|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Female|All|146.1|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||143.7|148.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|30.4|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|47.7|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|48.2|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|53.6|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|60.2|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|60.5|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|86.4|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|93.1|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||91.2|95.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|96.0|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2013 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||91.6|100.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|104.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|107.9|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2013|Male|All|126.3|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|47.9|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|50.8|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|59.4|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|60.2|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|92.9|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|96.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|100.3|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|101.9|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||98.8|105.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|124.3|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|138.2|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|All|167.1|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||165.2|168.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|American Indian/Alaska Native|57.8|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||American Indian alone||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Asian/PI|17.6|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Asian/PI|18.2|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Asian/PI|25.7|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Asian/PI|26.2|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||22.1|30.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Asian/PI|29.1|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Asian/PI|64.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||60.5|68.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|112.6|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|115.8|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|118.9|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|195.0|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|202.3|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|221.2|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|268.1|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||257.8|278.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Black|528.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||518.6|538.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|31.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|47.1|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|50.2|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|51.0|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|64.9|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|65.7|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||60.4|71.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|97.5|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||95.0|100.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Hispanic|120.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Other|3.9|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Other|10.6|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Other|56.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|Other|315.6|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||302.2|329.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|21.0|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|24.3|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|27.0|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||23.6|30.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|31.3|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|32.4|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|34.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|39.2|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Both|White|143.6|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||141.1|146.0
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|31.0|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|46.8|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|48.8|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|56.9|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|58.9|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|70.5|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|89.7|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|93.8|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|99.8|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||95.5|104.2
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|109.9|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|123.1|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Female|All|208.0|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||205.1|210.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|29.2|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services; SANDAG, Current Population Estimates.|Rates per 10,000 population. County age_adjusted rates per 10,000 2000 US standard population. Asthma emergency department visit refers to (principal diagnosis) ICD_9 code 493.|Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|48.7|Los Angeles, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|California Patient Discharge Data, Non-Public Version Years 2010-2014. Office of Statewide Health Planning and Development (OSHPD), Healthcare Information Resource Centre, Room 500, 818 K Street, Sacramento, CA 95814||ED visits include both those who were admitted and those who were not. Includes ED visits to hospitals licensed to provide emergency medical services located in the city of Los Angeles (hospital ZIP codes based on Parcels, ZIP Code Points, City Boundaries Polygons publically available at Los Angeles County GIS Portal website -  http://egis3.lacounty.gov/dataportal). |Other rate not assessable due to unavailability of compatible population denominators.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|53.5|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|60.9|Miami (Miami-Dade County), FL|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|61.7|Phoenix, AZ|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|64.7|Cleveland, OH|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Ohio Department of Health EpiCenter Database||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|96.1|Minneapolis, MN|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|98.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|103.3|Oakland (Alameda County), CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Asthma ED visits from OSHPD, Denominator = 2014 population data from ESRI|The primary diagnosis disposition was reviewed for the asthma ICD-9 codes 493.00 through 493.99|||98.9|107.8
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|116.4|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|124.1|New York City, NY|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents with primary diagnosis of ICD-9-CM 493 ; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and adge-adjusted to US 2000 Standard Population||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2014|Male|All|126.9|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||124.7|129.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|All|31.0|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|All|32.1|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of visits, not number of residents. These rates include ED visits through the end of September 2015, prior to the CMS mandated transition to ICD-10-CM.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|All|101.2|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|All|101.4|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|All|158.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||156.6|160.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Asian/PI|18.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Asian/PI|26.0|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Asian/PI|65.9|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||62.0|69.9
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Black|106.5|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Black|210.3|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Black|222.0|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Black|510.2|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||500.4|520.1
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Hispanic|32.6|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Hispanic|52.4|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Hispanic|99.0|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||96.4|101.5
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Hispanic|124.4|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Other|52.0|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|Other|270.3|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||257.9|282.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|White|21.4|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|White|33.6|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|White|41.0|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Both|White|131.4|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||129.0|133.7
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Female|All|30.0|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of visits, not number of residents. These rates include ED visits through the end of September 2015, prior to the CMS mandated transition to ICD-10-CM.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Female|All|32.6|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Female|All|96.7|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Female|All|105.7|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Female|All|194.5|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||191.7|197.3
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Male|All|29.3|San Diego County, CA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM code (J45) was used for the months of October-December.|||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Male|All|34.8|Denver, CO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). Compute rates per 10,000, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||These data are not de-duplicated; numerator is # of visits, not number of residents. These rates include ED visits through the end of September 2015, prior to the CMS mandated transition to ICD-10-CM.||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Male|All|104.8|Boston, MA|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Male|All|120.3|Kansas City, MO|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|||||||
Chronic Disease|Asthma Emergency Department Visit Rate (Age-Adjusted; Per 10,000)|2015|Male|All|122.7|Las Vegas (Clark County), NV|Age-adjusted rate of asthma-related ED visits using ICD-9-CM Codes: 493.0, 493.1, 493.2, 493.8, 493.0. (Numerator = Asthma-Related ED visits, Denominator = 2010 census population). ED Visits includes those that were discharged or admitted - specify if it is one or the other. Compute rates per 10,000, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||120.5|124.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|10.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.9|11.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.0|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||10.7|13.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.9|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.2|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|16.3|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|16.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||13.2|20.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|17.5|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||14.8|20.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|18.4|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|19.1|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.5|20.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|19.6|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|20.5|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|20.8|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|21.4|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|21.6|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||19.3|24.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|22.9|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|23.0|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates||||18.4|28.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|24.7|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||22.2|27.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|24.7|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|25.2|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|25.9|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.5|30.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|26.1|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|33.8|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|0.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|18.4|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|36.4|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 16. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||American Indian alone||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|109.8|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|6.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||2.0|10.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|6.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|10.5|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||8.7|12.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|11.4|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|12.3|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|13.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||7.0|25.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|15.5|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 16. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|16.4|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates||||9.4|26.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|19.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.9|25.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|22.2|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|22.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|21.3|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||13.5|29.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|21.5|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|27.6|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||23.4|31.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|29.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|29.9|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|31.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|34.7|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|34.7|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|36.1|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|36.4|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||28.2|46.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|37.6|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|39.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|40.0|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates||||29.6|52.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|40.0|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||22.0|67.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|40.8|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|41.1|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||29.1|56.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|41.4|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|41.6|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|43.2|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||32.7|56.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|46.2|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|56.3|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||42.2|73.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|0.0|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|10.7|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||6.6|14.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|15.8|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||11.4|21.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|19.7|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|20.1|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|22.9|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|25.8|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|27.1|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|28.3|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|28.6|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|29.7|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||17.8|41.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|30.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||25.4|35.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|34.4|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|35.2|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||30.6|39.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|20.0|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|11.2|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|14.5|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.9|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.4|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|8.6|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||6.9|10.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.0|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.2|11.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.3|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates||||6.0|19.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|12.3|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.8|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.9|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||11.2|17.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||10.9|19.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.6|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||12.9|16.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|16.3|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||13.3|19.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|17.7|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|17.8|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|18.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|18.5|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|18.9|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|19.1|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|19.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||16.4|22.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|20.8|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.4|26.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|27.0|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|35.9|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|7.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||6.1|9.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|9.5|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|11.3|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||9.7|13.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|13.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||9.6|18.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|14.6|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|14.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||13.0|16.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|15.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||11.8|19.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|16.3|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|16.7|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|16.8|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|17.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|17.8|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|17.9|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||15.2|20.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|18.6|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|20.3|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Bexar County level data|||17.3|23.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|20.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|21.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates||||15.9|28.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|23.7|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|23.9|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.7|30.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|25.7|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|37.8|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|12.8|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||10.4|15.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|12.9|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2010-2012|||10.9|15.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.7|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.5|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.1|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|20.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||15.1|27.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|21.1|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||16.5|26.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|22.8|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|23.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|24.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2010 mid-year population estimates||||17.5|33.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|24.6|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||21.9|27.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|24.9|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|25.8|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|26.1|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|26.4|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|26.6|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||22.5|30.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|27.2|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|28.8|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.1|36.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|28.8|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|29.4|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|30.0|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Bexar County level data|||25.7|34.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|30.6|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|13.2|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||11.5|14.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.6|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|17.9|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|18.1|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.4|21.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|18.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|18.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.3|20.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.0|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.0|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.5|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.7|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|20.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|20.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|20.9|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|21.0|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|21.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|21.8|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|24.0|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|25.3|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||21.5|29.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|25.6|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|25.7|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|26.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|26.9|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E15|PA Eddie-->Vital Statistics|||||24.4|29.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|27.0|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|29.2|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|33.1|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||28.1|38.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|37.8|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|15.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|25.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|36.5|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 16. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||American Indian alone||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|6.7|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||2.8|10.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|12.5|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|14.2|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|14.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|15.9|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|16.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|18.5|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|19.7|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|20.2|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|21.7|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E19|PA Eddie-->Vital Statistics|||||9.9|33.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|21.9|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|22.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|24.2|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.9|30.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|25.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||17.8|34.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|26.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|26.5|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|27.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|28.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|28.3|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|29.5|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||22.2|38.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|32.5|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|32.8|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|33.0|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|34.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E17|PA Eddie-->Vital Statistics|||||29.9|39.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|35.0|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|35.7|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|36.3|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|38.0|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|38.2|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|38.4|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|40.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|40.8|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|40.9|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||29.5|55.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|43.3|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|45.0|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|55.5|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||43.6|69.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|80.7|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|0.0|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|11.6|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|14.1|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||9.1|19.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|15.1|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|17.1|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|22.0|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|22.7|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|22.7|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|22.8|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E18|PA Eddie-->Vital Statistics|||||12.5|33.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|24.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|26.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|26.8|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|27.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|27.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||22.8|32.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|27.9|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|33.5|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|44.2|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|47.5|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|3.8|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|35.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|0.3|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|2.0|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||0.0|6.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|12.4|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.8|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|7.6|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|12.8|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||10.8|14.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.5|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.8|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.0|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.5|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.7|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||13.1|16.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.1|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.7|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||12.7|19.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|16.7|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|16.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|18.1|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|19.1|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|19.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|22.3|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E16|PA Eddie-->Vital Statistics|||||19.1|25.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|23.4|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|23.9|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||20.0|27.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|26.3|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|26.4|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.4|32.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|40.3|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|11.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||9.7|14.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|12.8|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.1|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|14.6|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|14.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|14.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|15.9|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.0|17.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|16.1|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|16.7|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||13.5|21.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.0|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.2|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.8|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|18.2|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|18.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|18.5|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||14.4|23.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|20.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|20.9|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|22.4|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|23.1|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|23.2|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|23.6|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E21|PA Eddie-->Vital Statistics|||||20.5|26.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|24.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|24.2|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|28.2|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.5|34.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|37.3|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|18.9|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|19.7|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.4|25.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|20.8|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|21.3|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|22.1|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||19.6|24.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|22.4|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|23.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|24.2|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|24.4|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|25.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|25.4|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|26.0|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|26.2|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|26.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|26.7|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|28.1|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|28.7|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|31.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E22|PA Eddie-->Vital Statistics|||||27.2|35.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|32.0|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|32.1|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|34.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|35.0|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||27.9|42.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|37.7|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|38.4|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|39.8|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||32.0|49.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.5|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||12.7|16.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.9|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|18.5|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|19.6|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.1|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.5|21.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.8|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|21.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|21.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|21.7|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|21.9|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|22.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|22.2|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E23|PA Eddie-->Vital Statistics|||||19.9|24.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|22.3|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|22.6|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|22.9|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|23.5|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||20.3|27.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|24.2|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||20.5|27.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|24.2|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|25.6|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|28.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|28.4|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|30.8|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|33.7|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||28.7|38.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|38.6|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|70.4|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||57.5|83.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|0.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|15.3|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|36.9|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 16. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||American Indian alone||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|139.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|170.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|11.7|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|13.3|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|14.1|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.1|20.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|14.4|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|15.2|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|15.7|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 16. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|18.7|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.4|23.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|18.8|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|24.5|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|24.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|27.9|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||18.2|40.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|32.8|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|36.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|27.0|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|28.5|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|30.3|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E25|PA Eddie-->Vital Statistics|||||26.1|34.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|32.8|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|33.2|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||23.1|46.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|33.5|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||23.7|43.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|35.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|36.1|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.2|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.3|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.6|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|38.6|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|39.3|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|39.6|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|39.7|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|43.3|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||34.1|54.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|43.9|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|44.5|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|46.6|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|47.6|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||37.0|60.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|48.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||37.2|62.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|51.5|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||21.6|80.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|70.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|71.3|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|74.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.5|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|12.9|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|13.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.7|18.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|14.3|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|18.7|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|21.7|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|23.4|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|24.9|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E26|PA Eddie-->Vital Statistics|||||14.5|35.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|26.1|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|26.9|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|27.2|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|31.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||26.8|36.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|32.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|34.0|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||17.6|59.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|34.1|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|40.5|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|49.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|35.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.3|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|8.8|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||0.0|18.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|15.9|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|24.0|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.0|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.4|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.1|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.4|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.1|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||11.0|15.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.7|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|14.3|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.3|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||9.5|24.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.7|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.8|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.9|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E24|PA Eddie-->Vital Statistics|||||13.2|18.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.0|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.7|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.9|18.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|17.0|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.2|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.0|22.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.5|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|19.1|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|19.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|20.1|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|21.5|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|22.0|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||18.3|25.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|24.9|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|28.5|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|29.6|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||23.8|35.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|41.6|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|10.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.8|13.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.7|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|13.2|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|15.6|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|16.0|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|16.2|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.3|18.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|17.7|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|17.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.1|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.3|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||14.3|23.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.7|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E29|PA Eddie-->Vital Statistics|||||16.0|21.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|18.9|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|19.6|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.8|24.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|19.9|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|20.1|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|21.0|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|21.5|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|21.6|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.0|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.2|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|22.8|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||17.1|29.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|25.6|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|28.4|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.6|35.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|33.4|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|16.6|Los Angeles, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|18.5|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||15.6|21.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|22.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|22.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|23.9|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|24.0|Houston, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|24.7|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|24.9|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||22.3|27.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|25.5|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Table 17. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|25.9|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.0|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.0|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.1|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.3|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.5|Washington, DC|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.7|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E30|PA Eddie-->Vital Statistics|||||22.7|30.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|27.6|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|29.4|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||23.8|35.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|30.5|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|32.6|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||25.9|39.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|36.9|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|37.7|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|39.4|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2011 mid-year population estimates||||30.5|50.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|40.8|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||32.9|50.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|44.1|Cleveland, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Medical Examiner's Office||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|13.1|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||11.4|14.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|14.4|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||13.0|15.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.3|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|18.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|18.9|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.4|20.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|19.4|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||17.2|23.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.2|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.3|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|21.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E14||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|21.5|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|21.9|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|23.9|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||21.5|26.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|25.7|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||22.0|29.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|25.8|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||20.7|30.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|26.0|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E31|PA Eddie-->Vital Statistics|||||23.5|28.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|28.1|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|28.6|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|30.2|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|32.0|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|35.0|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||29.9|40.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|0.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|23.5|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|34.1|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E17||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|81.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|6.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|9.4|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|10.1|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||5.0|15.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|13.9|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||11.9|16.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|15.4|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|15.8|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E18||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|16.2|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|20.3|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|20.3|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||15.8|25.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|21.6|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|21.8|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||13.7|33.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|27.1|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|73.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|17.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||11.0|23.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|19.5|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|29.1|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|30.8|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E33|PA Eddie-->Vital Statistics|||||26.5|35.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|31.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|33.6|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|33.9|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|34.1|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||26.3|43.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|35.3|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||25.4|47.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|35.5|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|36.4|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|36.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|37.5|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||29.3|47.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|39.0|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|39.5|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E19||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|40.9|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||30.4|54.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|43.5|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|44.8|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|45.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|54.2|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||43.1|67.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|56.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|59.5|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||37.3|90.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|62.6|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||50.1|77.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|0.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|11.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|15.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||10.3|21.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|17.0|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|19.9|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|22.0|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|22.4|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||17.0|28.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|23.2|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||11.6|41.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|24.8|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|25.1|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||20.9|29.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|25.5|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|25.9|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|26.3|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E20||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|31.0|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|31.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E34|PA Eddie-->Vital Statistics|||||20.2|42.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|33.2|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||24.9|43.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|53.9|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|56.7|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|8.2|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|62.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|0.0|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|1.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|9.6|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||0.0|22.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.7|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||7.0|10.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.8|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||10.8|15.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.1|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||8.1|21.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|14.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|14.8|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.7|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|16.0|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.3|17.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.2|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||15.7|20.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.5|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E21||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.9|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|19.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|20.3|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E32|PA Eddie-->Vital Statistics|||||17.2|23.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|20.7|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|21.9|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|22.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|23.8|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|24.8|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||20.9|28.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|26.8|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.7|32.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|11.5|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||9.3|13.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|12.0|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||10.3|13.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|13.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|14.1|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.3|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.4|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||12.2|19.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.7|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.9|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.1|17.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|17.3|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|17.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E15||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.0|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.1|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|19.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|19.7|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||15.6|24.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|21.1|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|21.3|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||18.2|24.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|21.9|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E37|PA Eddie-->Vital Statistics|||||18.9|24.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|22.7|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||17.0|29.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|23.7|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|26.1|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|26.7|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|28.8|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|29.5|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||23.7|36.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.7|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||12.1|17.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.0|San Francisco, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Value is reported for a multi-year period, 2013-2015|||14.8|19.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.8|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|21.1|Phoenix, AZ|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|22.4|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||19.9|24.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.1|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.2|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Diabetes Mortality Rate ICD-10 codes: E10-E14, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.3|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.4|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.8|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|25.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2013, Tables 16-17, E10-E16||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||21.0|31.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.3|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.5|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||22.6|30.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.7|Chicago, Il|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|29.4|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2012 mid-year population estimates||||22.0|38.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|31.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|32.2|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E38|PA Eddie-->Vital Statistics|||||27.7|36.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|33.1|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||26.6|39.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|35.2|San Jose, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|36.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|36.7|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|41.7|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||33.8|51.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.8|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.4|11.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|16.6|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|17.4|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||16.0|18.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|18.1|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.4|21.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|19.0|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||15.7|22.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|19.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|19.9|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|20.0|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|20.9|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|20.9|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E21||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|21.3|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|23.0|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||20.6|25.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|23.2|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||19.7|26.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|23.4|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E40|PA Eddie-->Vital Statistics|||||21.0|25.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|24.4|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|25.8|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||20.8|30.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|28.1|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Bexar County level data|||25.6|30.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|33.2|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|35.4|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||30.3|40.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|0.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|94.0|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|7.3|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||2.5|12.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|7.5|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|11.1|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|15.0|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E25||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|16.6|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||9.6|28.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|18.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.7|23.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|25.4|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||16.8|37.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|26.1|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|62.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|18.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||11.6|25.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|30.5|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||23.2|39.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|31.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|31.3|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E42|PA Eddie-->Vital Statistics|||||13.7|19.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|34.9|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|35.7|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|35.7|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|36.3|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|38.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E26||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|38.9|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|40.1|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||29.4|53.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|42.4|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|42.8|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||31.2|57.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|43.2|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||31.7|57.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|49.4|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|49.7|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||38.9|62.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|52.0|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||32.9|78.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|53.1|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||41.6|66.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|55.9|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||33.6|87.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|11.5|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||7.1|15.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|17.2|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|17.4|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|18.2|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||8.7|33.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|20.3|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|22.5|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.7|67.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|23.3|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||19.3|27.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|23.4|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E43|PA Eddie-->Vital Statistics|||||27.0|35.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|25.1|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E27||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|26.0|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||18.8|35.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|37.5|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||33.2|41.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|41.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|44.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|55.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|0.0|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|2.8|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||0.0|8.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.9|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.3|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||7.6|11.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.9|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|13.7|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|14.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||12.9|16.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|14.9|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||12.0|18.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.2|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.8|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||9.7|24.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.9|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||12.4|20.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.4|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E41|PA Eddie-->Vital Statistics|||||21.0|25.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||13.4|19.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.8|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.9|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|18.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E28||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|18.7|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||16.2|21.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|20.9|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||17.4|24.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|25.9|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|30.3|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||24.5|36.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.7|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||5.9|9.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|10.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|12.7|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||11.1|14.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|13.3|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|15.9|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||12.0|21.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|16.7|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||13.2|20.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|16.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|17.0|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|17.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E23||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|17.3|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|18.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.3|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||15.3|24.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.9|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||17.0|22.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|20.6|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E46|PA Eddie-->Vital Statistics|||||17.7|23.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|22.1|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|22.1|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||16.5|28.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|24.2|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Bexar County level data|||21.1|27.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|26.4|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.0|32.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|28.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|12.2|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||9.9|14.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.0|Long Beach, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.7|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.4|25.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|22.9|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||17.5|29.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|23.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||21.0|26.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|23.6|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|23.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|25.6|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nat'l Vital Statistics Report, Final Deaths 2014, Tables 16-17, E10-E22||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|26.3|Miami (Miami-Dade County), FL|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|27.1|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||23.1|31.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|27.4|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDS Wonder: E10-E14|||||21.9|33.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|27.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E47|PA Eddie-->Vital Statistics|||||23.5|31.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|27.6|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|29.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|30.8|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|31.0|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County Vital Statistics|Diabetes mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14||||23.3|40.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|33.5|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Bexar County level data|||29.2|37.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|41.0|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes E10-E14|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|47.1|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||37.8|56.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||10.7|14.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|17.3|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||14.2|21.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|20.0|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|20.1|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|20.3|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|20.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.9|22.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|21.0|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|21.3|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|22.4|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||20.1|24.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|22.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E48|PA Eddie-->Vital Statistics|||||20.2|24.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|23.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates||||19.2|28.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|25.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Bexar County level data|||23.0|27.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|26.0|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||22.7|29.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|26.7|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|26.8|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14|||||23.0|30.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|33.3|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|36.6|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||31.3|41.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|34.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|64.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|12.4|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||6.8|18.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|13.4|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|13.8|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|14.3|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||8.1|24.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|15.7|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|15.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||12.2|20.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|18.7|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates||||11.4|28.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|85.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|23.2|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||15.6|33.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|27.7|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||19.1|36.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|29.4|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E50|PA Eddie-->Vital Statistics|||||25.3|33.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|31.2|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||23.5|40.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|34.4|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|35.1|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|35.7|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|35.9|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|37.0|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|39.2|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||23.9|61.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|39.6|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|40.2|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||31.7|50.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|41.7|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|42.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates||||31.6|56.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|48.5|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||37.4|61.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|49.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||37.9|63.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|80.8|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14|||||52.8|118.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|10.3|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||6.1|14.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|15.2|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E51|PA Eddie-->Vital Statistics|||||8.2|22.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|20.5|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|24.3|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||9.1|63.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|25.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|25.6|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|29.1|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|29.2|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||21.8|38.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|32.1|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||27.5|36.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|34.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||30.3|38.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|52.9|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|2.0|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||0.0|6.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|6.7|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|8.8|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||7.1|10.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|11.2|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|12.5|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.3|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates||||8.0|20.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|14.7|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||11.4|19.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|15.6|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|15.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.1|17.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|16.4|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||13.5|19.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|16.8|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E49|PA Eddie-->Vital Statistics|||||14.1|19.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|17.9|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|18.9|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|21.2|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||18.6|23.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|22.4|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|22.5|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||19.0|26.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|23.8|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14|||||20.0|27.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|33.2|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||27.1|39.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|10.5|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||8.4|12.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|14.7|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||11.0|19.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|15.1|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|16.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||14.6|18.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|16.9|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|17.3|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|17.4|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|17.9|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|19.1|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||16.3|21.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|19.3|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E54|PA Eddie-->Vital Statistics|||||16.5|22.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|20.9|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||17.1|25.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|22.0|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||19.1|24.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|22.3|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates||||16.7|29.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|23.1|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|23.6|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14|||||18.9|28.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|27.6|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.0|34.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|28.8|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|14.9|Las Vegas (Clark County), NV|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Nevada Vital Records - Clark County Deaths|||||12.2|17.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|20.2|Seattle, WA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||15.2|26.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|24.2|New York City, NY|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|24.4|Boston, MA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|24.7|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2015 mid-year population estimates||||18.1|33.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|25.4|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|25.5|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||22.9|28.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|26.0|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|26.2|U.S. Total, U.S. Total|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|26.4|Fort Worth (Tarrant County), TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|National Center for Health Statistics|||||22.6|30.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|27.0|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E55|PA Eddie-->Vital Statistics|||||23.0|30.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|29.9|San Antonio, TX|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||25.9|33.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|30.6|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder E10-E14|||||24.4|36.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|31.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|33.5|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||27.7|40.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|39.7|Detroit, MI|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|49.2|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||39.6|58.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|20.4|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||18.9|21.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|21.4|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E56|PA Eddie-->Vital Statistics|||||19.2|23.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|21.7|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|22.9|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder|||||19.6|26.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|23.1|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||18.5|27.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|24.9|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|26.4|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||23.1|30.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|27.9|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|29.6|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||24.9|34.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|224.7|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|19.4|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|19.8|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||12.4|30.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|21.8|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||17.6|26.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|32.8|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|26.3|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E58|PA Eddie-->Vital Statistics|||||22.5|30.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|39.6|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||29.4|52.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|41.6|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|45.3|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||34.3|58.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|47.0|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||36.4|59.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|48.3|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|48.7|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||39.6|59.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|59.5|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|21.5|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||10.7|38.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|23.0|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|24.9|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E59|PA Eddie-->Vital Statistics|||||15.5|34.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|32.2|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||27.7|36.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|35.3|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|45.9|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|11.2|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||6.3|18.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|14.2|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|15.1|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||13.5|16.7
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|16.1|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E57|PA Eddie-->Vital Statistics|||||13.3|18.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|19.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|19.9|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|20.3|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||16.9|24.2
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|21.4|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder|||||18.0|24.9
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|23.4|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.6|29.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|12.2|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder|||||9.3|15.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|15.7|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||13.9|17.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|17.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||14.8|20.1
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|17.7|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||12.8|23.8
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|19.2|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||15.6|23.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|20.2|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.4|26.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|21.0|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|26.2|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|25.6|Denver, CO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|26.4|San Diego County, CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||23.8|29.0
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|26.5|Philadelphia, PA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|PA Eddie-->Vital Statistics|||||22.6|30.4
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|29.3|Minneapolis, MN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Minnesota Vital Statistics||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|29.9|Oakland (Alameda County), CA|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Alameda County vital statistics files|Using 2016 mid-year population estimates||||22.8|38.6
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|30.7|Kansas City, MO|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|||||||
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|35.6|Indianapolis (Marion County), IN|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|MCPHD Death Certificate data|||||29.7|42.3
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|36.8|Portland (Multnomah County), OR|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|CDC Wonder|||||30.2|43.5
Chronic Disease|Diabetes Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|42.7|Columbus, OH|Diabetes mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: E10-E14|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||34.5|52.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|95.5|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|106.0|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|107.5|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|128.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||120.0|138.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|139.4|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|142.0|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||130.0|154.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|145.1|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|148.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||143.7|152.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|149.8|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||145.4|154.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|171.2|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|172.3|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|174.1|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|182.7|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|183.5|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||174.3|193.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|190.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||183.7|196.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|203.0|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||190.7|215.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|214.3|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|219.5|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|230.2|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||222.7|237.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|331.4|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|352.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||343.2|362.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|42.1|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|234.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|39.0|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|51.5|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|71.3|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||55.5|90.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|73.1|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|86.3|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||68.3|109.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|87.0|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|90.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|92.4|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|105.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||93.4|118.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|106.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||78.4|134.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|108.2|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||91.1|125.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|123.3|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||117.0|129.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|154.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||105.2|219.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|97.1|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|98.1|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|158.8|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|195.1|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|199.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|199.8|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||158.7|249.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|200.0|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||179.2|222.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|208.7|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|213.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||184.5|242.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|219.4|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|223.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||196.6|250.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|225.9|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||199.5|252.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|229.5|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|245.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||220.1|271.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|264.5|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||251.6|277.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|275.8|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|280.9|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|288.9|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||265.1|314.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|291.3|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|315.6|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|378.6|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||338.1|419.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|33.5|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|76.9|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|102.4|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|108.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||76.0|150.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|116.6|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|119.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||103.9|134.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|119.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||109.5|130.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|126.0|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||112.8|140.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|129.4|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|131.8|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|134.8|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|149.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|150.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||86.4|247.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|151.7|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||124.5|179.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|157.6|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|327.3|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||312.9|341.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|17.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|39.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||16.2|63.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|70.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race|||20.2|192.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|109.6|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|117.5|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|132.9|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|69.4|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|110.6|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|127.7|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||117.5|139.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|133.9|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||113.0|154.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|136.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|137.1|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|154.5|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||149.3|159.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|155.7|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|155.7|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||148.7|163.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|160.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|170.5|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|176.7|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|182.4|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||172.0|193.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|185.4|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|186.1|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|200.8|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||186.1|215.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|208.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||200.1|216.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|213.2|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|224.3|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||214.3|234.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|226.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||216.7|236.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|373.7|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||359.3|388.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|429.9|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|80.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|82.5|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|93.8|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|99.2|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||89.4|110.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|110.6|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|114.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|116.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||102.7|130.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|119.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||114.8|125.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|122.2|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||117.1|127.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|133.6|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|139.3|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|141.2|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||133.4|149.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|144.5|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|151.4|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||140.9|162.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|153.2|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||139.4|167.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|154.2|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|167.1|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|183.1|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|186.0|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||177.5|194.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|289.1|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||277.7|300.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|309.4|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|96.2|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|135.6|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|139.6|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|168.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||152.7|186.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|174.9|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2010 mid-year population estimates||||153.9|195.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|178.6|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|181.2|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2010-2012|||173.6|189.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|181.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|183.2|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||175.8|190.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|206.8|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|218.0|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|219.5|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|221.1|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|224.2|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||208.0|241.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|246.0|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||235.1|257.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|271.2|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||248.1|294.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|271.7|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|277.2|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|292.8|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||279.1|306.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|355.3|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|439.6|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||422.4|456.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|101.3|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|102.6|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|115.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|119.8|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|129.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|130.4|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|140.8|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|142.1|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||133.2|151.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|143.7|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|149.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||144.7|153.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|153.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|156.1|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|157.7|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|159.9|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|167.8|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||159.1|176.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|175.0|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|188.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|194.8|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|195.8|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|197.0|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||190.3|203.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|199.1|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||186.8|211.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|210.8|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|225.8|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||218.4|233.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|301.4|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|356.6|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|14.9|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|54.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|197.1|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|40.3|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|44.6|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|51.3|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|63.1|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||41.6|91.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|67.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||47.1|88.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|74.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|77.7|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|81.7|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|96.8|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|97.1|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||85.9|108.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|100.9|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|118.2|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|119.6|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|126.0|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||106.3|145.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|130.2|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|109.1|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|141.8|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|143.0|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|154.3|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|159.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||117.8|210.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|174.0|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|181.5|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|182.8|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|190.7|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|201.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||173.1|229.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|201.5|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||176.6|226.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|205.7|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||184.9|228.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|215.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|220.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|223.1|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|226.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|235.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|236.6|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|237.8|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||225.8|249.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|248.5|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|259.3|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|262.9|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||236.5|289.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|291.0|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|305.0|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|339.7|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|28.0|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|50.8|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|94.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|97.3|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|97.8|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|104.8|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|105.4|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|106.3|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|113.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|115.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||100.3|130.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|116.9|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|118.7|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|120.1|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||110.1|130.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|124.0|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|132.8|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|145.1|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|145.5|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|152.9|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|172.3|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||143.8|200.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|30.3|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|54.8|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|3.6|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|37.8|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|40.8|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||16.7|65.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|93.8|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|114.1|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|241.1|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Other only includes American Indian or Alaska Native.|||157.5|353.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|549.2|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|66.7|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|110.5|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|127.8|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|130.6|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|133.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|144.4|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|144.5|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|145.4|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|146.9|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||137.3|156.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|149.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|150.1|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|153.2|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|159.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||153.7|164.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|159.6|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||149.9|169.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|164.8|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|166.0|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|178.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|199.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|204.1|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||189.3|219.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|211.7|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|215.6|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|216.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||208.2|225.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|268.4|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|464.4|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|82.0|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|83.3|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|93.4|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|93.6|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|94.3|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|95.7|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|104.0|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||94.3|113.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|112.1|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|114.8|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||109.9|119.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|118.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|120.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|123.3|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|125.7|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|128.7|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|134.4|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||124.4|145.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|143.8|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|144.4|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|149.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||141.3|157.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|157.7|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|159.4|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||145.2|173.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|164.7|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|165.1|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|174.2|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||166.1|182.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|243.8|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|330.2|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|109.4|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|125.0|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|152.2|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|157.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|176.6|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|178.3|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|179.1|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics Files, 2011-2013||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|193.2|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||185.7|200.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|195.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||178.5|211.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|198.0|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|198.5|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|199.8|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|204.3|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|211.1|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|213.7|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||198.1|230.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|214.7|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|234.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|245.9|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|251.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||240.2|262.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|253.4|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||231.0|275.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|257.7|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|274.3|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|299.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||285.7|313.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|377.7|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|385.2|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|92.8|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|103.7|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|114.6|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|117.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|131.8|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|133.0|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|136.5|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|141.4|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||137.2|145.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|143.2|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|143.5|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||134.6|152.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|158.1|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|162.4|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|169.3|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|170.5|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|171.9|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|174.2|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||165.4|183.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|178.9|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|180.3|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|188.2|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|198.1|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||185.9|210.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|201.2|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||194.5|208.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|203.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||196.6|210.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|210.5|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|218.0|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|241.6|Baltimore, MD|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|334.6|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|339.2|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|359.0|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||329.2|388.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|33.4|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|119.6|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||American Indian alone||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|192.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|255.5|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|42.9|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|45.0|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|66.8|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|67.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||47.8|87.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|79.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||62.9|99.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|82.9|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||58.6|113.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|92.2|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|95.8|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|98.2|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|102.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||90.9|113.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|114.2|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|130.6|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|132.5|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||112.0|152.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|145.1|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|151.4|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|183.4|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|131.6|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|133.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|152.4|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|156.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|159.6|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|163.6|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|169.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||127.5|220.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|195.6|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||168.8|222.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|198.9|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|205.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|205.8|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|206.0|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||185.3|228.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|207.1|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||182.1|232.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|211.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||185.6|237.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|216.3|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 17. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by Hispanic origin, race for non-Hispanic population, and sex: United States, 2012||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|221.7|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|227.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|229.1|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|229.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||217.7|241.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|240.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|266.7|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|270.0|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|285.1|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||258.1|312.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|288.5|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|325.5|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|348.0|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|31.8|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|39.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|72.4|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|80.9|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|85.3|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|95.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||52.0|159.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|107.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||76.4|147.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|115.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||105.4|124.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|117.5|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|122.0|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 17. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by Hispanic origin, race for non-Hispanic population, and sex: United States, 2012||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|124.3|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|130.7|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|137.1|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||120.1|154.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|139.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|140.9|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|143.3|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|145.1|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|147.0|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|147.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||121.8|173.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|150.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|159.4|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|14.3|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|2.1|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|42.4|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||20.2|64.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|84.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|115.7|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|125.8|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|233.4|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Other only includes American Indian or Alaska Native.|||151.0|344.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|63.5|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|111.0|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|118.2|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|121.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|134.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||114.6|154.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|144.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|145.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|145.9|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||136.4|155.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|149.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||144.0|154.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|150.9|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|153.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|156.2|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|159.0|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|169.7|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||159.7|180.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|172.3|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 17. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by Hispanic origin, race for non-Hispanic population, and sex: United States, 2012||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|175.5|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|177.7|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||169.0|186.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|179.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|181.3|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|190.5|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|201.7|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||187.0|216.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|202.6|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|206.0|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|214.5|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||206.1|222.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|265.6|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|432.6|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|71.7|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|88.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|94.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|98.3|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|104.9|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|108.3|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||103.6|113.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|110.6|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|110.9|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||100.8|121.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|113.7|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|117.3|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||103.5|131.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|122.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|126.9|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|126.9|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|133.3|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||123.4|143.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|135.5|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|136.7|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|142.8|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|143.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|147.5|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||139.5|155.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|155.6|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|156.0|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||148.3|163.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|158.6|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||144.6|172.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|176.6|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|330.7|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|107.1|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|118.6|Los Angeles, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|139.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|156.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|166.9|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|168.5|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|172.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2011 mid-year population estimates||||152.0|193.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|182.4|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|183.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||175.7|190.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|187.3|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||171.1|203.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|205.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|208.1|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|214.7|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|217.0|Houston, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate for Harris County. Age adjustment uses 2000 standard population.|Harris County data, not just Houston||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|223.4|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|228.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|230.7|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|231.6|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||215.4|248.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|231.7|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|249.7|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||227.7|271.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|264.7|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||253.2|276.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|273.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||260.3|286.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|276.1|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|279.3|Washington, DC|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|348.4|Cleveland, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Medical Examiner's Office||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|106.4|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|122.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|128.0|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|133.0|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|133.2|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||129.0|137.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|136.2|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||124.7|147.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|138.5|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||129.9|147.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|139.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|139.2|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|143.3|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||139.2|147.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|158.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|160.9|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|162.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||155.8|168.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|163.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|169.8|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|171.4|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||162.7|180.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|175.6|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|184.2|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|195.6|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|204.9|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||192.4|217.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|208.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||201.5|215.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|217.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||210.5|224.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|314.1|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|26.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|36.5|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||American Indian alone; *All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|120.6|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||American Indian alone||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|237.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|47.3|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|54.1|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|58.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||39.9|77.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|69.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|72.0|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||49.6|101.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|72.2|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||56.8|90.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|74.2|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||50.4|105.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|79.2|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|90.5|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|92.8|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|94.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||83.9|104.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|97.1|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|100.9|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|102.6|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|115.4|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||109.4|121.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|117.0|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|152.5|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||130.5|174.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|110.8|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|126.5|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|134.8|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|149.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|152.4|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|165.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||123.3|216.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|169.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||148.7|189.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|190.8|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|196.4|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|202.6|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||182.2|224.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|204.3|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||178.5|230.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|206.9|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||181.6|232.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|207.0|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||187.1|228.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|208.5|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|215.5|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 17. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by Hispanic origin, race for non-Hispanic population, and sex: United States, 2012||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|216.7|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|221.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|226.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||198.3|255.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|235.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||223.6|247.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|237.0|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|248.1|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|309.4|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||280.5|338.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|320.8|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|29.7|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|49.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|55.7|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|88.8|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|96.6|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||80.7|112.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|120.2|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||110.7|129.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|121.2|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 17. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by Hispanic origin, race for non-Hispanic population, and sex: United States, 2012||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|122.6|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|129.8|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||96.6|170.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|129.9|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|131.9|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||118.5|146.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|142.2|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|148.2|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|150.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|151.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||126.0|176.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|153.1|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|155.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|159.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||141.3|178.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|24.6|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|35.5|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|204.2|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||127.2|281.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|3.0|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|74.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|78.0|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||44.7|111.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|85.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|203.8|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Other only includes American Indian or Alaska Native.|||131.9|300.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|123.2|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||104.3|142.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|128.6|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|134.9|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||128.4|141.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|139.0|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|142.0|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||132.6|151.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|143.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|150.5|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||145.4|155.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|151.8|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|154.4|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|154.9|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|160.8|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|161.6|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|165.7|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||155.8|176.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|171.8|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 17. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by Hispanic origin, race for non-Hispanic population, and sex: United States, 2012||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|172.4|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||164.7|180.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|175.7|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|177.0|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|181.7|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||172.9|190.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|198.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|199.2|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|210.2|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||195.1|225.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|225.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||216.7|233.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|266.7|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|90.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|90.5|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|97.3|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|97.9|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|101.6|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||88.7|114.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|102.0|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||92.5|111.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|102.5|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||97.8|107.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|106.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|111.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||107.0|116.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|115.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|125.6|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|126.3|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|126.6|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|131.1|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||123.6|138.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|132.4|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||122.7|142.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|134.3|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|136.4|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|152.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|155.2|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|161.6|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||147.3|175.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|163.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||155.2|172.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|168.3|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||160.3|
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|245.9|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|113.0|Phoenix, AZ|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Final Year End Death Data||*All races,except for white, contain Hispanic/Latino populations||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|163.1|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|166.3|San Jose, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|167.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|170.3|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||Value is reported for a multi-year period, 2013-2015|||163.0|178.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|183.2|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||176.0|190.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|184.1|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2012 mid-year population estimates||||163.1|205.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|189.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||172.9|205.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|193.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|202.6|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||191.0|214.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|204.6|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Heart Disease Mortality Rate ICD-10 codes: I00-I09, I11, I13, I20-I51,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|206.4|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|213.4|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|214.5|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012Con. [Age-adjusted rates per 100,000 U.S. standard population; see Technical||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|215.9|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|221.6|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|222.9|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||207.2|239.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|226.4|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|250.0|Chicago, Il|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|267.6|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||244.5|290.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|268.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||255.6|281.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|280.5|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||268.6|292.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|405.8|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2013 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics for Bridged Race Population Estimates||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|111.0|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|117.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||109.3|126.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|124.6|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|131.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||127.8|135.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|133.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51||||122.2|145.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|136.7|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||128.2|145.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|154.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|158.5|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|158.6|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||152.3|164.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|166.1|Houston, TX|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Suggested ICD-10 codes: I00-I109, I11, I13, I20-I51|||Harris County data, not just Houston|||161.5|170.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|167.0|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|169.3|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||160.8|178.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|178.0|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|179.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|181.0|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|207.5|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||194.9|220.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|211.0|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||203.9|218.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|225.8|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||218.6|233.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|307.1|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|374.7|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||365.3|384.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|119.1|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|264.0|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|64.9|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||44.1|92.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|67.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|71.9|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||49.2|101.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|72.2|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||56.8|91.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|72.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Represents Asiain population alone. Does not include Pacific Islander population|||57.0|91.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|86.0|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||64.1|108.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|86.1|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|86.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||77.5|96.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|98.4|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|111.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|112.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|125.6|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|194.7|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||147.5|252.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|197.8|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||172.3|223.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|132.0|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|133.8|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|155.7|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||115.9|204.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|167.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||132.9|210.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|190.1|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|194.5|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||172.7|216.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|200.6|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||174.7|226.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|206.7|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|209.2|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51||||182.7|235.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|210.8|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|211.1|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||190.8|233.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|220.0|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||193.9|246.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|227.0|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|231.8|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||220.3|243.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|237.3|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|281.6|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|287.2|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||259.6|314.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|315.4|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|430.1|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||390.6|469.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|47.7|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|52.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|96.6|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||81.0|112.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|98.4|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51||||69.3|135.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|107.2|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||98.4|115.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|110.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||62.2|185.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|111.1|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||65.8|175.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|116.0|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|133.0|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|139.0|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|147.3|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|151.5|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|159.8|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||141.5|178.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|163.2|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||137.9|188.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|351.5|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||338.0|365.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|129.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||71.3|187.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|53.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|73.5|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||41.3|105.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|121.4|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||48.0|331.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|159.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Other only includes American Indian or Alaska Native.|||100.2|242.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|103.5|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|121.5|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||111.8|132.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|125.7|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51||||105.6|145.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|139.5|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||134.6|144.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|140.7|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||131.5|149.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|143.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|145.6|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|151.6|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|159.0|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|160.3|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||150.7|170.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|166.6|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||159.0|174.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|169.9|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|179.6|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|186.7|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||177.7|195.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|196.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|210.6|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||195.6|225.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|238.7|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||229.9|247.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|253.0|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|389.0|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||374.8|403.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|30.4|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I52||||||27.9|33.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|79.3|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|81.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||72.3|90.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|93.2|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|97.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||93.3|102.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|101.6|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51||||88.6|114.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|102.4|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||92.9|111.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|113.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|117.2|Cleveland, OH|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Suggested ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|121.8|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||114.7|129.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|125.1|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|131.8|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|132.4|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||122.6|142.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|132.6|Houston, TX|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Suggested ICD-10 codes: I00-I109, I11, I13, I20-I51|||Harris County data, not just Houston|||127.2|138.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|143.6|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|146.8|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|152.7|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|158.9|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||144.8|172.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|161.7|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||153.8|169.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|162.0|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||153.7|170.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|247.7|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|305.1|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||294.1|316.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|155.8|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|166.9|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|167.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||152.5|184.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|175.4|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||168.5|182.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|176.9|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County Vital Statistics|Heart disease related mortality rate per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51||||156.2|197.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|183.4|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDS Wonder: I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||167.6|199.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|202.3|Miami (Miami-Dade County), FL|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|206.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|206.8|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||195.4|218.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|208.7|Houston, TX|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Suggested ICD-10 codes: I00-I109, I11, I13, I20-I51|||Harris County data, not just Houston|||200.7|206.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|210.9|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Table 16. Age-adjusted death rates for 113 selected causes, Enterocolitis due to Clostridium difficile, drug-induced causes, alcohol-induced causes, and injury by firearms, by race and sex: United States, 2012-Con. [Age-adjusted rates per 100,000 U.S. standard population]||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|211.0|Long Beach, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|219.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|229.8|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|272.4|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||249.3|295.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|281.5|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||268.4|294.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|301.1|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||288.9|313.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|386.4|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: I00-I109, I11, I13, I20-I51|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|468.3|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||451.9|484.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|109.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||101.2|117.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|126.6|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|127.6|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||116.6|138.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|132.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||129.1|136.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|134.5|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|149.6|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||140.7|158.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|157.6|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||151.5|163.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|163.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|163.5|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||155.1|172.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|168.5|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|181.4|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|188.2|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|208.1|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||201.1|215.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|227.9|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||220.6|235.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|231.1|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||217.9|244.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|296.6|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|373.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||364.7|383.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|208.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|61.3|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||41.4|87.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|72.7|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|73.1|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||58.1|90.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|78.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||58.4|99.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|80.3|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|83.8|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||74.9|92.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|87.1|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||70.4|107.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|88.5|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||64.6|118.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|101.0|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|115.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|169.2|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||146.2|192.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|174.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||133.8|224.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|116.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||87.8|151.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|129.5|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|140.3|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||104.2|185.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|155.8|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|177.5|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||153.8|201.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|197.3|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||171.5|223.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|198.9|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||177.3|220.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|208.5|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||188.4|230.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|213.8|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|219.1|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||208.0|230.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|230.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|243.8|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||216.3|271.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|252.2|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|295.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||267.4|323.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|302.4|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|425.4|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||386.6|464.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|51.0|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||22.2|104.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|98.4|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||70.3|134.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|99.2|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||91.2|107.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|103.2|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||87.5|118.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|106.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|111.3|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|136.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||113.5|159.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|142.1|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|159.5|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|172.1|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||153.1|191.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|347.7|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||334.6|360.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|114.7|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||56.7|172.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|42.3|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||20.2|64.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|151.2|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|166.1|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||105.3|249.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|267.8|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||113.7|563.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|111.9|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||102.7|122.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|118.8|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|124.8|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||105.2|144.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|145.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||140.1|149.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|153.5|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|153.5|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||143.9|163.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|156.5|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||146.9|166.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|157.9|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|162.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|166.0|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||158.6|173.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|195.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||186.3|204.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|197.5|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|208.7|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||200.4|217.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|234.0|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||218.2|249.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|251.4|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|391.4|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||377.2|405.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|32.6|San Francisco, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I53||||||30.0|35.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|80.6|Seattle, WA|Per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Suggested ICD-10 codes: I00-I109, I11, I13, I20-I51||||||72.1|90.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|96.7|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|100.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||87.7|113.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|102.6|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|105.6|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||101.1|110.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|117.5|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||107.4|127.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|119.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|126.0|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||118.8|133.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|133.6|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|134.5|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||124.7|144.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|139.3|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|149.3|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|163.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||156.1|171.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|170.7|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||156.0|185.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|172.8|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||164.2|181.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|239.0|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|303.6|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||292.7|314.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|94.0|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|68.5|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|165.7|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|93.0|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|135.6|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|148.3|Seattle, WA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||134.0|164.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|161.9|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2015 mid-year population estimates||||142.7|181.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|166.7|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||160.1|173.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|171.1|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|179.9|Boston, MA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|189.1|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-I51 (Not sure what is meant by ICD-10 code I109)|||||173.3|204.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|198.4|Fort Worth (Tarrant County), TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|National Center for Health Statistics|||||187.4|209.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|202.6|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||187.9|218.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|211.8|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|221.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|222.4|New York City, NY|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|NYC DOHMH Bureau of Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|252.2|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|271.8|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||259.0|284.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|293.6|Las Vegas (Clark County), NV|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Nevada Vital Records - Clark County Deaths|||||281.5|305.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|319.5|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||294.5|344.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|375.6|Detroit, MI|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|464.9|San Antonio, TX|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder||Bexar County level data|||448.9|481.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|American Indian/Alaska Native|148.0|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Asian/PI|109.7|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Black|258.6|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Hispanic|146.4|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|White|216.3|U.S. Total, U.S. Total|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Health, United States, 2016, HHS/CDC/NCHS, Table 22 https://www.cdc.gov/nchs/data/hus/2016/022.pdf||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|122.7|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|132.5|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||128.8|136.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|135.1|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||123.9|146.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|155.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|157.7|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||149.4|166.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|163.0|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||153.8|172.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|185.8|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|215.8|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||208.7|222.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|219.2|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||206.3|232.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|408.1|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|72.0|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||57.6|88.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|76.9|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||57.6|96.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|81.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|82.5|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||73.9|91.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|105.3|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||80.4|135.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|111.2|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|175.0|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|188.4|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||146.0|239.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|188.8|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||170.0|209.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|195.6|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||171.0|220.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|222.8|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||195.5|250.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|237.3|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|248.5|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|249.5|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||221.8|277.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|254.0|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||242.1|266.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|97.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|101.3|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||93.1|109.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|128.4|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||97.0|166.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|142.6|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||93.1|208.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|144.6|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||121.5|167.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|144.7|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|17.4|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|117.5|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||60.7|205.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|126.0|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|189.1|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Other only includes American Indian or Alaska Native.|||123.5|277.1
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|110.2|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|115.1|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||96.2|133.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|143.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||139.1|148.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|149.8|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|152.8|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||143.3|162.8
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|161.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|165.6|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||155.7|175.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|183.3|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||174.3|192.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|213.7|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||198.6|228.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|88.0|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|103.0|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||98.6|107.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|109.0|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||95.7|122.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|109.6|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|121.8|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||112.6|131.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|127.2|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||116.7|137.7
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|141.3|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|170.3|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||155.6|184.9
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|171.2|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||163.1|179.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|168.9|San Diego County, CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||162.4|175.5
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|169.2|Oakland (Alameda County), CA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Alameda County vital statistics files|Using 2016 mid-year population estimates||||149.9|188.6
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|169.9|Minneapolis, MN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Minnesota Vital Statistics||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|206.6|Indianapolis (Marion County), IN|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|MCPHD Death Certificate data|||||191.6|222.4
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|208.8|Portland (Multnomah County), OR|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|CDC Wonder I01, I05-I09, I11, I13, I20-51, (not sure what is meant by I00-I109)|||||192.4|225.2
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|214.4|Denver, CO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from the Colorado Department of Public Health and Environment||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|245.7|Kansas City, MO|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|||||||
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|279.4|Philadelphia, PA|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|PA Eddie-->Vital Statistics|||||266.5|292.3
Chronic Disease|Heart Disease Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|292.0|Columbus, OH|Heart disease related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 codes: I00-I109, I11, I13, I20-I51|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||267.9|316.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|18.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||14.0|22.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|20.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||18.1|22.5
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|26.1|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2010-2012.||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|26.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2010 NC BRFSS (Mecklenburg Sample)|||||21.8|31.6
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|29.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|29.3|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|30.6|Houston, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey. Available at https://www.dshs.state.tx.us/Layouts/ContentPage.aspx?pageid=35474. Accessed on May 22, 2015.|crude rate of BMI>=30 in Houston-Baytown-Sugarland MSA.  Data were displayed with Percent (CI 95%, n). Respondents 18 years and older who have a BMI calculated by self reported height and weight.  The '*' indicates that data is not available. The sample size includes all survey respondents except those with missing, |don't know| or refused answers|Data for the Houston-Baytown-Sugarland MSA, not just Houston|||27.9|33.5
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|31.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|32.2|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Public Health Management Corporation (PHMC) Household Health Survey||2014 data are actually 2014-15||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|32.4|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||28.4|36.5
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|All|33.6|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Asian/PI||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS||Does not include Pacific Islander; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|28.5|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||17.3|43.1
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|32.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||27.4|37.3
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|32.8|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|35.5|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|37.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||20.0|58.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|38.2|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|38.4|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Public Health Management Corporation (PHMC) Household Health Survey||2014 data are actually 2014-15||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|39.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2010 NC BRFSS (Mecklenburg Sample)|||||28.5|50.3
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Black|45.0|Houston, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey. Available at https://www.dshs.state.tx.us/Layouts/ContentPage.aspx?pageid=35474. Accessed on May 22, 2015.|crude rate of BMI>=30 in Houston-Baytown-Sugarland MSA.  Data were displayed with Percent (CI 95%, n). Respondents 18 years and older who have a BMI calculated by self reported height and weight.  The '*' indicates that data is not available. The sample size includes all survey respondents except those with missing, |don't know| or refused answers|Data for the Houston-Baytown-Sugarland MSA, not just Houston|||37.2|53.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|28.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||21.9|35.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|29.2|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|30.6|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|35.9|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Public Health Management Corporation (PHMC) Household Health Survey||2014 data are actually 2014-15||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|38.7|Houston, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey. Available at https://www.dshs.state.tx.us/Layouts/ContentPage.aspx?pageid=35474. Accessed on May 22, 2015.|crude rate of BMI>=30 in Houston-Baytown-Sugarland MSA.  Data were displayed with Percent (CI 95%, n). Respondents 18 years and older who have a BMI calculated by self reported height and weight.  The '*' indicates that data is not available. The sample size includes all survey respondents except those with missing, |don't know| or refused answers|Data for the Houston-Baytown-Sugarland MSA, not just Houston|||32.0|45.7
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|39.6|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||32.5|47.1
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic|45.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||25.0|66.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Other|11.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS||Multiple Race|||4.0|25.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|15.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||12.6|18.4
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|16.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||12.0|21.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|20.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2010 NC BRFSS (Mecklenburg Sample)|||||14.9|25.3
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|25.3|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|25.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|26.4|Houston, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey. Available at https://www.dshs.state.tx.us/Layouts/ContentPage.aspx?pageid=35474. Accessed on May 22, 2015.|crude rate of BMI>=30 in Houston-Baytown-Sugarland MSA.  Data were displayed with Percent (CI 95%, n). Respondents 18 years and older who have a BMI calculated by self reported height and weight.  The '*' indicates that data is not available. The sample size includes all survey respondents except those with missing, |don't know| or refused answers|Data for the Houston-Baytown-Sugarland MSA, not just Houston|||23.2|30.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|26.8|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Public Health Management Corporation (PHMC) Household Health Survey||2014 data are actually 2014-15||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|29.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||24.5|34.3
Chronic Disease|Percent of Adults Who Are Obese|2010|Both|White|31.8|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|16.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2010 NC BRFSS (Mecklenburg Sample)|||||12.5|21.1
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|21.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||18.5|24.1
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|24.8|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|29.7|Houston, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey. Available at https://www.dshs.state.tx.us/Layouts/ContentPage.aspx?pageid=35474. Accessed on May 22, 2015.|crude rate of BMI>=30 in Houston-Baytown-Sugarland MSA.  Data were displayed with Percent (CI 95%, n). Respondents 18 years and older who have a BMI calculated by self reported height and weight.  The '*' indicates that data is not available. The sample size includes all survey respondents except those with missing, |don't know| or refused answers|Data for the Houston-Baytown-Sugarland MSA, not just Houston|||26.4|33.2
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|31.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|32.8|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|33.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||28.7|39.4
Chronic Disease|Percent of Adults Who Are Obese|2010|Female|All|34.4|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Public Health Management Corporation (PHMC) Household Health Survey||2014 data are actually 2014-15||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|18.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||14.0|24.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|19.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||15.8|22.7
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|25.1|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|29.3|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Public Health Management Corporation (PHMC) Household Health Survey||2014 data are actually 2014-15||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|30.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||25.1|37.1
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|31.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|31.5|Houston, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey. Available at https://www.dshs.state.tx.us/Layouts/ContentPage.aspx?pageid=35474. Accessed on May 22, 2015.|crude rate of BMI>=30 in Houston-Baytown-Sugarland MSA.  Data were displayed with Percent (CI 95%, n). Respondents 18 years and older who have a BMI calculated by self reported height and weight.  The '*' indicates that data is not available. The sample size includes all survey respondents except those with missing, |don't know| or refused answers|Data for the Houston-Baytown-Sugarland MSA, not just Houston|||27.3|36.0
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|34.1|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2010|Male|All|35.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2010 NC BRFSS (Mecklenburg Sample)|||||26.6|44.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|11.6|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||7.9|15.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|20.3|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).||||17.3|23.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|20.9|Minneapolis, MN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS SMART County Prevalence Data|Respondents classified as obese based on body mass index. (30.00 <= _BMI5 < 99.99). Variable (2012, 2011): _BMI5CAT; Variable (2010): _BMI4CAT|County data was used as a proxy (Hennepin County)||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|21.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|21.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|21.1|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||16.5|25.6
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|21.6|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|23.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|23.8|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|24.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||21.3|26.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|24.6|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|25.2|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|25.6|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2011 NC BRFSS (Mecklenburg Sample)|||||21.0|30.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|27.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.2|27.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|29.2|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|30.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||27.0|33.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|30.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||23.8|37.4
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|31.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||28.9|37.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|37.3|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|All|39.7|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.||||||36.5|43.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|American Indian/Alaska Native|35.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||33.0|37.9
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|American Indian/Alaska Native|51.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|American Indian alone||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI|6.8|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI|8.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]|Asian alone|||7.5|10.1
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI|9.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI|11.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI|13.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||4.2|21.9
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|15.3|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).||||6.4|24.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|22.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||11.0|33.6
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|29.2|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|32.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.8|41.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|33.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|34.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|34.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2011 NC BRFSS (Mecklenburg Sample)|||||25.8|43.8
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|35.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||30.2|40.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|35.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|36.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||31.0|41.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|36.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|37.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||36.4|38.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|39.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.||||||35.8|43.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|40.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|41.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||28.6|53.4
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|43.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||22.7|59.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black|46.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|13.3|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|24.2|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|28.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|29.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|29.3|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).||||21.7|36.9
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|29.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|30.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||29.1|31.1
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|30.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.4|37.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|32.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||24.9|39.8
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|35.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|36.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||29.4|43.4
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|41.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||31.0|41.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic|47.2|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.||||||28.8|66.4
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Multiracial|25.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Multiracial|30.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||28.2|32.6
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Multiracial|32.2|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other|12.2|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other|19.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other|21.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||18.9|25.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other|26.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other|44.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||27.2|62.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|10.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|11.3|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||6.6|16.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|13.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|14.8|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|17.1|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|17.8|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||14.7|20.9
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|18.0|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).||||13.5|22.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|18.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2011 NC BRFSS (Mecklenburg Sample)|||||13.9|23.8
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|19.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||17.0|22.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|21.3|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||16.3|26.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|21.5|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||18.0|26.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|22.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|25.3|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|25.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||20.7|31.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|26.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||25.9|26.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|32.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||24.2|40.9
Chronic Disease|Percent of Adults Who Are Obese|2011|Both|White|33.9|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.||||||24.6|44.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|11.7|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||6.0|17.3
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|17.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|20.3|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).||||16.9|23.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|21.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|23.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||19.7|26.9
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|23.7|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||17.2|30.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|24.5|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|24.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|25.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|27.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||26.8|27.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|28.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2011 NC BRFSS (Mecklenburg Sample)|||||22.0|34.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|28.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|28.4|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|33.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||29.0|37.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|35.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||29.9|41.1
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|36.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||27.2|46.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|39.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Female|All|46.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.||||||42.1|50.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|11.5|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||6.4|16.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|18.3|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||11.2|25.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|18.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|20.3|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Asked of respondents who are 18 years or older. Provides Body Mass Index (BMI) (by dividing WEIGHT(in kgs) by HEIGHT SQUARED(in meters).||||14.9|25.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|20.5|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|21.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|21.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|23.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2011 NC BRFSS (Mecklenburg Sample)|||||15.9|30.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|24.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||20.6|28.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|24.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||15.3|34.5
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|25.8|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|26.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|27.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||23.0|32.0
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|27.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.4|28.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|31.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||24.6|38.2
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|32.5|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.||||||27.8|37.7
Chronic Disease|Percent of Adults Who Are Obese|2011|Male|All|35.0|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|9.4|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||6.0|12.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|19.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|21.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||16.4|26.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|21.7|Minneapolis, MN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS SMART County Prevalence Data|Respondents classified as obese based on body mass index. (30.00 <= _BMI5 < 99.99). Variable (2012, 2011): _BMI5CAT; Variable (2010): _BMI4CAT|County data was used as a proxy (Hennepin County)||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|21.9|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|22.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|22.7|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)||||18.9|26.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|24.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|25.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|26.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2012 NC BRFSS (Mecklenburg Sample)|||||21.9|30.6
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|27.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||24.9|29.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|27.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.4|28.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|29.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||24.5|34.6
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|29.4|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|30.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||27.0|33.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|30.7|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|31.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||24.8|37.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|All|41.0|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||35.4|46.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|American Indian/Alaska Native|33.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||31.4|36.1
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI|9.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]|Asian alone|||8.5|11.2
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI|10.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI|22.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||11.7|32.6
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|27.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|32.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||27.4|37.3
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|33.2|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2012 NC BRFSS (Mecklenburg Sample)|||||23.8|42.6
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|33.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|34.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|34.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|35.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||26.5|44.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|37.0|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|37.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||36.8|38.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|38.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||33.0|42.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|38.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|41.4|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||35.2|47.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|45.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||30.7|60.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black|47.9|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||27.1|68.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|25.0|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|27.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|27.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|27.2|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)||||19.9|34.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|27.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||22.3|32.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|28.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||15.8|40.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|28.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||21.9|35.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|29.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|30.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||21.0|41.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|30.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||29.3|31.3
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|33.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||25.9|41.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic|33.8|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Multiracial|29.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.6|31.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Multiracial|34.2|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other|16.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other|22.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||12.3|32.2
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other|23.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other|24.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||21.5|27.1
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other|43.2|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||21.5|68.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|9.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|13.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|15.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||12.6|18.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|18.1|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|19.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|20.8|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)||||16.1|25.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|22.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|22.5|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2012 NC BRFSS (Mecklenburg Sample)|||||17.4|27.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|23.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||20.0|26.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|23.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||16.7|30.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|24.5|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||18.2|32.2
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|26.0|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||19.5|32.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|26.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||26.2|26.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|27.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||24.0|30.3
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White|34.8|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||21.2|51.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Both|White||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|8.7|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||4.0|13.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|19.0|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)||||14.7|23.3
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|19.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|20.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|21.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||14.9|28.2
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|22.5|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2012 NC BRFSS (Mecklenburg Sample)|||||17.1|27.8
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|24.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|25.5|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|27.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|27.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.9|30.9
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|27.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.0|27.7
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|31.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||24.4|38.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|34.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|35.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||26.4|44.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|36.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||32.0|40.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Female|All|40.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||34.0|47.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|10.0|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||4.8|15.3
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|17.9|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|18.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|20.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|21.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||14.4|28.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|23.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||20.0|28.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|26.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|26.4|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Obese Adults (BMI < 30)||||20.3|32.5
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|27.0|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|27.0|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||17.9|36.1
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|27.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.8|31.0
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|27.7|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||21.1|35.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|28.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.6|28.4
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|30.5|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2012 NC BRFSS (Mecklenburg Sample)|||||23.7|37.3
Chronic Disease|Percent of Adults Who Are Obese|2012|Male|All|41.5|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||32.2|51.5
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|11.5|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||7.5|15.5
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|19.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|20.7|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)||||15.7|25.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|21.7|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||20.0|23.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|22.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|22.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|22.9|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|23.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|23.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||17.6|29.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|23.8|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|24.5|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|26.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.1|29.2
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|28.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||28.0|28.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|29.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||26.0|32.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|30.9|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||27.4|34.3
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|31.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||25.9|37.3
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|31.6|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||26.7|36.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|34.7|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|All|37.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||31.8|43.2
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|American Indian/Alaska Native|34.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||32.4|37.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI|8.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI|9.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]|Asian alone|||8.4|10.9
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI|10.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI|10.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI|12.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||4.8|20.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI|15.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||8.9|21.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)/ solely respresentative of pacific islander population||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|27.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|27.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|28.2|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|30.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|32.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2013 NC BRFSS (Mecklenburg Sample)|||||22.6|41.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|32.5|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|33.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||29.3|36.8
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|34.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|36.0|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||29.9|42.5
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|36.4|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|36.8|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||18.0|55.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|37.0|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||28.9|45.1
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|37.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||36.8|38.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|39.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||28.4|50.8
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|40.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||35.0|46.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black|40.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||30.4|51.5
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|15.3|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|24.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|24.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||17.5|31.8
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|25.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||15.0|39.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|25.6|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|27.0|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)||||15.5|38.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|27.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||23.1|31.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|28.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|30.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|31.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||30.5|32.5
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|34.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|35.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||19.2|51.8
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|36.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic|41.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|2013 BRFSS Bexar County||2011, 2012 results not included for standardization purposes||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Multiracial|31.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||29.3|34.2
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other|15.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other|16.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other|20.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other|24.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||9.9|39.8
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other|24.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||21.8|28.1
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|9.8|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|14.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|14.0|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|15.0|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||8.6|21.5
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|16.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||13.9|18.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|16.8|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)||||11.3|22.3
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|17.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|19.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2013 NC BRFSS (Mecklenburg Sample)|||||13.5|24.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|20.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|21.1|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||12.0|30.2
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||17.0|26.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|23.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|23.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|24.2|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|2013 BRFSS Bexar County||2011, 2012 results not included for standardization purposes||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|27.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||26.8|27.3
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|27.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.7|31.1
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|29.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||21.8|36.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|29.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||25.6|33.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Both|White|41.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||27.2|57.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|18.8|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)||||12.4|25.1
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|20.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|21.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||14.3|28.8
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|22.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|22.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2013 NC BRFSS (Mecklenburg Sample)|||||16.0|28.6
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|23.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||20.7|25.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|23.7|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|24.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|24.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|24.9|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|27.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||22.7|31.4
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|27.2|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|28.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.9|28.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|33.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||29.0|38.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|33.3|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||28.6|38.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|36.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||28.0|44.2
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|36.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All|47.1|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||39.5|54.9
Chronic Disease|Percent of Adults Who Are Obese|2013|Female|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|16.0|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||9.8|22.1
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|16.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS||This survey is not conducted annually.||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|20.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.|||17.7|22.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|20.2|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|20.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|21.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Based on self-reported height and weight; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|22.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|22.8|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Obese Adults (BMI < 30)||||14.9|30.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|23.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|Age-adjusted|||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||20.0|29.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|24.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2013 NC BRFSS (Mecklenburg Sample)|||||18.1|31.2
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|25.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|AZ BRFSS|2011, 2012, 2013; Percent of adults 18 and over who are classified as obese based on BMI.|All Maricopa County||||
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|25.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||21.1|29.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|26.2|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||15.4|37.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|26.5|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||19.5|35.0
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|26.8|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||18.9|34.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|28.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||23.1|33.1
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|28.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.9|28.7
Chronic Disease|Percent of Adults Who Are Obese|2013|Male|All|33.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|12.1|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||5.7|18.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|17.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||14.7|19.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|18.3|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||15.1|22.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|19.8|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||12.2|27.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|22.5|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|used crude rates|2014 BRFSS.  Obesity = BMI of 30+|||19.9|22.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|23.0|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)||||18.9|27.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|24.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2014 NC BRFSS (Mecklenburg Sample)|||||19.4|28.6
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|24.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||23.4|26.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|28.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||28.6|29.2
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|29.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||26.0|32.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|31.4|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||26.4|36.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|32.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||28.9|35.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|32.2|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||26.8|38.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|32.4|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||29.2|35.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|33.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||29.0|37.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|34.7|Kansas City, MO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|All|35.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||28.4|42.2
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|American Indian/Alaska Native|33.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||31.2|35.6
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI|3.6|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||1.0|12.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI|6.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||4.8|8.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI|9.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]|Asian alone|||8.2|10.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI|17.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||6.3|28.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)/ solely respresentative of pacific islander population||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|26.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||16.5|35.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|30.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2014 NC BRFSS (Mecklenburg Sample)|||||21.1|39.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|33.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||30.4|36.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|33.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||27.6|40.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|37.7|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||25.7|51.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|38.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||28.1|48.7
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|38.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||37.9|39.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|40.3|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||33.1|47.6
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|41.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||35.0|48.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black|45.4|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||31.3|59.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Black||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic|21.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||16.0|26.9
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic|25.6|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)||||18.0|33.3
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic|31.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||28.9|34.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic|32.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||31.2|33.2
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic|32.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||25.0|39.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic|33.9|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||29.3|38.9
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Hispanic||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Multiracial|30.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||28.2|32.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other|21.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||8.4|34.6
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other|25.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||16.5|36.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other|25.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||22.2|29.7
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|Other||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|13.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||10.5|16.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|19.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||17.8|22.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|19.8|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||16.0|24.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|19.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2014 NC BRFSS (Mecklenburg Sample)|||||14.2|25.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|20.3|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||11.9|28.6
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|21.9|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)||||16.7|27.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||18.0|28.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|27.7|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||23.4|32.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|27.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||27.5|28.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|29.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||25.2|32.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|29.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||26.0|33.6
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White|33.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||24.4|41.7
Chronic Disease|Percent of Adults Who Are Obese|2014|Both|White||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|17.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||13.0|22.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|20.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||16.6|24.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|21.6|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2014 NC BRFSS (Mecklenburg Sample)|||||15.8|27.3
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|24.4|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)||||18.5|30.3
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|25.3|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|used crude rates|2014 BRFSS.  Obesity = BMI of 30+|||21.4|29.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|25.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||21.8|30.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|26.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||24.4|28.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|28.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||15.0|41.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|28.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||28.5|29.2
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|31.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||26.9|35.7
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|34.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||30.3|38.9
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|36.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||31.0|41.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|36.4|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||29.1|44.3
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All|40.4|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||31.3|49.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Female|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|11.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||4.7|18.3
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|14.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||11.2|17.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|19.7|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|used crude rates|2014 BRFSS.  Obesity = BMI of 30+|||16.2|23.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|21.5|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Obese Adults (BMI < 30)||||15.8|27.3
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|22.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||20.9|24.7
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|26.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2014 NC BRFSS (Mecklenburg Sample)|||||19.6|34.1
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|27.2|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS|The proportion of adults whose BMI was greater than or equal to 30.0.||||19.6|36.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|29.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|BRFSS, accessed via the CDC's Nutrition, Physical Activity and Obesity Data, Trends and Maps website|Obese is defined as a Body Mass Index (BMI) greater than or equal to 30.0; BMI was calculated from self-reported weight and height [weight (kg)/ height (m^2)]||||28.6|29.4
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|29.4|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||18.9|39.8
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|29.9|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||25.3|34.5
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|30.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||24.0|37.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|32.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||27.5|36.9
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All|33.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||28.4|38.0
Chronic Disease|Percent of Adults Who Are Obese|2014|Male|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|15.2|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||12.8|17.9
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|18.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||13.7|22.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|18.4|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS|||||9.1|27.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|21.9|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||19.9|24.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|22.2|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).||||||19.6|24.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|22.7|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||12.0|33.5
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|22.9|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)||||19.7|26.1
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|24.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||23.0|25.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|26.2|Long Beach, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Source: 2011 Los Angeles County Health Survey, 2015 Los Angeles County Health Survey.  Note: 2011 estimates are based on self-reported data by a random sample of 8,036 Los Angeles County adults, representative of the adult population in Los Angeles County.   2015 estimates are based on self-reported data by a random sample of 8,008 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|27.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||23.0|32.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|27.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.5|30.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|28.5|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||24.4|32.6
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|29.7|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||24.0|35.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|30.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2015 NC BRFSS (Mecklenburg Sample)|||||24.4|35.9
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|31.1|Kansas City, MO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|33.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||26.6|40.6
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|All|35.6|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||30.2|41.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI|3.8|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey,  2015, Boston Public Health Commission||This survey is conducted every other year.|||0.5|7.1
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI|6.4|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||2.7|14.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI|7.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||5.4|9.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI|13.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||2.6|23.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)/ solely respresentative of pacific islander population||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|10.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||4.6|16.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|17.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||8.9|32.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|26.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||16.0|36.2
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|31.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey,  2015, Boston Public Health Commission||This survey is conducted every other year.|||26.7|35.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|33.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||19.8|48.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|33.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||31.3|36.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|35.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||28.0|43.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|40.0|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||30.3|49.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black|46.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2015 NC BRFSS (Mecklenburg Sample)|||||35.2|56.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|25.3|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)||||19.8|30.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|31.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||29.3|33.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|32.6|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||18.9|50.1
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|33.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey,  2015, Boston Public Health Commission||This survey is conducted every other year.|||26.9|39.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|36.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||27.6|44.5
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|37.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||25.2|50.2
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic|41.4|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||35.5|49.9
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other|18.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS||Includes Asian/PI|||0.0|37.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other|19.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||7.4|31.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other|22.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||15.6|30.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other|24.5|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)||||10.3|38.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|Other||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|12.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||8.1|17.2
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|15.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||13.1|18.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|17.8|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey,  2015, Boston Public Health Commission||This survey is conducted every other year.|||14.9|20.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|18.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||16.7|20.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||17.0|28.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|22.9|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)||||18.7|27.1
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|25.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2015 NC BRFSS (Mecklenburg Sample)|||||17.3|33.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|25.7|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||21.1|30.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|25.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||21.0|30.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|28.7|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||21.1|37.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White|35.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||26.6|44.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Both|White||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|13.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||10.6|17.5
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|19.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||12.6|25.5
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|19.9|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).||||||16.5|23.2
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|22.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Data is for Alameda County (excluding Berkeley)|||14.1|30.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|22.2|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)||||17.4|26.9
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|24.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey,  2015, Boston Public Health Commission||This survey is conducted every other year.|||21.6|27.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|25.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||20.7|29.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|25.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||24.0|27.1
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|28.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||23.2|34.1
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|29.4|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||21.4|37.5
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|32.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||26.0|38.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|32.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||25.5|39.5
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All|33.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2015 NC BRFSS (Mecklenburg Sample)|||||25.4|40.6
Chronic Disease|Percent of Adults Who Are Obese|2015|Female|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|16.6|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||13.1|20.6
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|17.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||11.4|23.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|19.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|Boston Behavioral Risk Factor Survey,  2015, Boston Public Health Commission||This survey is conducted every other year.|||16.2|22.3
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|23.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||17.0|29.0
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|23.6|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Obese Adults (BMI < 30)||||19.3|27.9
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|24.8|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).||||||20.1|28.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|27.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|2015 NC BRFSS (Mecklenburg Sample)|||||18.4|35.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|28.3|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||22.1|34.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|28.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Nevada BRFSS - Clark County|||||23.3|34.4
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|37.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||26.5|48.7
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All|39.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS survery data||Bexar County level data|||30.8|47.8
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults who have BMI 30 or higher (obese).|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Alameda County (excluding Berkeley)||||
Chronic Disease|Percent of Adults Who Are Obese|2015|Male|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|All|18.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||15.8|21.1
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|All|26.9|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County. |||12.4|41.5
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|All|28.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||24.0|33.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|All|31.6|Kansas City, MO|BRFSS (or similar survey). Percent of population 18 years and over that is obese.|||||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|All|33.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||27.3|39.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Black|32.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||21.1|43.4
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Black|34.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||21.9|46.5
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Black|36.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||30.0|44.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Hispanic|21.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||16.2|27.3
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Other|14.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS||Includes Asian/PI|||3.3|25.1
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|White|15.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||12.1|18.2
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|White|23.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||18.0|30.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|White|32.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||24.9|39.5
Chronic Disease|Percent of Adults Who Are Obese|2016|Both|White||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Female|All|19.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||15.4|23.1
Chronic Disease|Percent of Adults Who Are Obese|2016|Female|All|31.0|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||23.4|38.5
Chronic Disease|Percent of Adults Who Are Obese|2016|Female|All|36.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||30.0|42.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Female|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Are Obese|2016|Male|All|17.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|Colorado BRFSS|Colorado BRFSS||||14.2|21.3
Chronic Disease|Percent of Adults Who Are Obese|2016|Male|All|20.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|PA Eddie-->BRFSS |||||15.0|26.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Male|All|35.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|BRFSS|||||26.4|44.0
Chronic Disease|Percent of Adults Who Are Obese|2016|Male|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that is obese (BMI 30 or greater).|California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of adults (age 18+ yrs) who have BMI 30 or higher (obese).|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|10.6|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|12.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2010 NC BRFSS (Mecklenburg Sample)|||||8.4|15.7
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|12.6|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|13.0|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. BRFSS Data. 2011. [accessed 3/13/17].|||||10.6|15.3
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|14.5|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|16.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||13.4|19.8
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|19.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.  Percent estimate is in response to current smoking of 1+ cigarette per day during the past month|||16.6|21.5
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|20.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|24.3|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All|25.2|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|All |13.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS|||||10.0|16.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Asian/PI|13.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Asian/PI||Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS||Does not include Pacific Islander; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|10.9|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|11.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2010 NC BRFSS (Mecklenburg Sample)|||||5.2|16.8
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|13.4|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||6.6|25.2
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|19.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.  Percent estimate is in response to current smoking of 1+ cigarette per day during the past month|||15.0|23.9
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|27.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS|||||15.0|45.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|27.7|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black|29.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A|N/A|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Hispanic|9.3|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Hispanic|13.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS|||||4.0|33.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Hispanic|16.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||11.6|22.4
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Hispanic|17.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.  Percent estimate is in response to current smoking of 1+ cigarette per day during the past month|||11.8|23.2
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Hispanic|21.5|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Hispanic||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A|N/A|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Other|13.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS||Multiple Race|||5.0|34.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Other|21.8|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A|N/A|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|11.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2010 NC BRFSS (Mecklenburg Sample)|||||7.3|15.3
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|11.9|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|12.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS|||||9.0|16.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|17.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||13.2|22.4
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|19.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|22.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.  Percent estimate is in response to current smoking of 1+ cigarette per day during the past month|||18.3|25.9
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|24.2|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White|25.1|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Both|White||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A|N/A|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|8.3|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|9.4|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|12.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS|||||8.0|17.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|12.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||9.5|15.8
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|14.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2010 NC BRFSS (Mecklenburg Sample)|||||9.3|20.6
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|15.9|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|16.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.  Percent estimate is in response to current smoking of 1+ cigarette per day during the past month|||13.2|18.8
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|21.8|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All|24.3|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Female|All||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A|N/A|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|8.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2010 NC BRFSS (Mecklenburg Sample)|||||4.4|12.3
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|13.2|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|16.0|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|20.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Data is pulled from the Texas State Health & Human Services website for the San Antonio Metropolitan statistical area (MSA): http://healthdata.dshs.texas.gov/HealthRisks/BRFSS ||Bexar County level data|||15.8|27.0
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|22.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission||This survey is not conducted annually.  Percent estimate is in response to current smoking of 1+ cigarette per day during the past month|||18.4|26.6
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|25.1|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|26.3|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Public Health Management Corporation (PHMC) Household Health Survey||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All|27.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|N/A|N/A|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2010|Male|All |13.0|Seattle, WA|Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker|BRFSS|||||9.0|18.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|11.9|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||8.5|15.3
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|12.7|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|13.4|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|14.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|14.8|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||10.8|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|15.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2011 NC BRFSS (Mecklenburg Sample)|||||12.1|19.7
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|16.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|17.1|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|17.3|Minneapolis, MN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS SMART County Prevalence Data|Respondents who reported having smoked at least 100 cigarettes in their lifetimeand currently smoke. Variable: _RFSMOK3|County data was used as a proxy (Hennepin County)||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|17.7|Long Beach, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Source: 2011 Los Angeles County Health Survey.  Note: 2011 estimates are based on self-reported data by a random sample of 8,036 Los Angeles County adults, representative of the adult population in Los Angeles County.  The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|19.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|20.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|20.8|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|22.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||19.6|24.9
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|22.7|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|23.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|26.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||19.7|32.9
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|All|30.8|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||25.6|36.5
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|American Indian/Alaska Native|25.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|American Indian alone||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|American Indian/Alaska Native|26.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||American Indian alone||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI|7.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI|9.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI|13.2|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI|14.0|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI|21.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||10.7|31.8
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|7.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|15.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|18.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|19.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|19.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||9.0|29.2
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|19.2|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|20.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2011 NC BRFSS (Mecklenburg Sample)|||||12.1|27.9
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|20.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||9.4|31.8
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|23.5|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|26.0|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|29.0|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||23.5|35.2
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|29.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|29.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||20.8|37.8
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|30.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||25.0|36.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Black|30.8|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|11.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|12.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|14.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|15.2|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|15.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|16.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|18.6|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|22.2|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic|28.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||17.0|41.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Multiracial|18.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Other|10.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Other|17.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Other|18.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Other|18.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Other|21.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||11.8|31.7
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|9.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||5.3|12.7
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|9.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|14.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2011 NC BRFSS (Mecklenburg Sample)|||||9.5|19.1
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|14.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|15.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||10.2|21.8
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|15.3|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|17.9|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|20.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||16.0|24.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|21.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|21.3|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|22.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|22.8|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|25.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||22.0|29.1
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|26.8|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||18.5|35.1
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Both|White|42.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||26.1|60.5
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|7.2|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||3.9|10.6
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|9.3|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|10.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||5.9|16.9
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|10.7|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|12.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|13.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|14.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|15.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2011 NC BRFSS (Mecklenburg Sample)|||||10.9|20.7
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|16.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|17.1|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|17.4|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Healthy Chicago Survey, 2014||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|19.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||16.0|22.2
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|23.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||17.8|30.6
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||20.0|28.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|26.9|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Female|All|27.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||18.5|36.1
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|14.8|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|16.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2011 NC BRFSS (Mecklenburg Sample)|||||10.2|21.9
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|16.8|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||10.7|23.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|17.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|17.8|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|19.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|19.6|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||13.3|28.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|20.6|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|21.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|25.1|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|25.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||15.2|35.2
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|25.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||21.2|29.4
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|26.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|26.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||21.0|31.0
Chronic Disease|Percent of Adults Who Currently Smoke|2011|Male|All|38.5|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||30.2|47.6
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|11.9|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||0.1|0.2
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|12.3|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|13.2|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||10.6|16.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|15.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|16.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|16.4|Minneapolis, MN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS SMART County Prevalence Data|Respondents who reported having smoked at least 100 cigarettes in their lifetimeand currently smoke. Variable: _RFSMOK3|County data was used as a proxy (Hennepin County)||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|17.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||15.1|19.1
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|18.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|18.5|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|19.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|20.0|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|20.2|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2012 NC BRFSS (Mecklenburg Sample)||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|20.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|21.8|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||22.0|27.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|26.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||20.5|32.4
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|All|29.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||24.0|35.2
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|American Indian/Alaska Native|18.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||American Indian alone||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|American Indian/Alaska Native|33.9|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI|1.5|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI|6.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||3.7|12.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI|8.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||3.3|18.5
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI|8.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||3.1|13.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI|10.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI|12.0|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|14.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|17.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|18.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||8.1|37.8
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|19.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||11.6|29.6
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|19.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||15.0|23.9
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|23.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||16.1|30.1
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|25.5|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|25.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|26.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||14.2|38.8
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|27.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||23.0|32.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|27.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|27.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2012 NC BRFSS (Mecklenburg Sample)|||||18.8|36.8
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|29.1|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|31.4|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||25.4|38.1
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Black|35.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||15.7|55.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|11.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||8.0|15.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|12.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|13.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|14.5|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|14.9|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|15.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|17.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||11.8|23.2
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|17.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||10.2|28.9
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|21.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|24.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||16.0|34.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Multiracial|18.6|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||9.0|34.4
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Multiracial|31.0|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Other|12.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Other|12.6|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||5.1|27.8
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Other|16.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Other|20.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Other|23.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||11.6|34.4
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|10.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|11.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||10.1|14.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|12.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||9.8|16.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|16.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|17.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|17.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|19.0|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|19.1|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2012 NC BRFSS (Mecklenburg Sample)|||||14.9|23.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|20.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||17.3|23.1
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|20.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|21.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|22.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||18.3|25.9
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White|26.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||18.7|33.5
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Both|White||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|9.5|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||7.5|11.9
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|10.4|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|11.1|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||4.7|17.4
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|11.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|12.5|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||8.9|17.2
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|12.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|13.2|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|15.9|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|15.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|16.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||13.7|18.6
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|17.0|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|17.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|19.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2012 NC BRFSS (Mecklenburg Sample)|||||14.5|24.8
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|21.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||14.5|29.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||19.0|26.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Female|All|26.5|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||20.6|33.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|12.8|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||7.2|18.5
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|13.8|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||10.3|18.3
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|14.4|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|County BRFSS data||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|15.4|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for a range of years, 2012-2014|||12.9|18.2
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|18.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||14.9|21.1
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|19.2|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|19.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|20.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2012 NC BRFSS (Mecklenburg Sample)|||||15.1|25.6
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|20.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|22.6|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|24.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|24.2|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|27.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||23.0|31.0
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|28.7|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.  Current smoking calculated variable (_RFSMOK3, calculated by BRFSS), |adults who are current smokers.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|31.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||21.9|40.4
Chronic Disease|Percent of Adults Who Currently Smoke|2012|Male|All|32.8|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||23.9|43.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|10.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|12.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||0.1|0.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|13.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|14.0|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|14.8|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|16.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|16.9|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||13.0|20.8
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|17.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|18.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||16.6|20.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|18.8|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|19.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||17.0|22.6
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|20.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|23.5|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||20.3|26.7
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||21.0|28.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|28.9|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||23.6|34.8
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All|30.0|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||24.6|35.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|American Indian/Alaska Native|21.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||American Indian alone||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|American Indian/Alaska Native|34.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI|5.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI|5.8|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI|9.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI|14.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI|14.7|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||8.4|20.9
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI|16.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||7.1|25.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|8.9|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|9.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|12.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|15.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|17.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|19.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||16.0|22.5
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|22.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2013 NC BRFSS (Mecklenburg Sample)|||||13.7|30.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|22.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||14.1|31.1
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|28.4|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|28.5|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||21.0|36.1
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|29.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||23.6|36.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|30.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||25.0|36.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|37.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|40.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||30.0|51.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black|40.7|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||20.0|61.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|11.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|13.2|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|14.2|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|14.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|14.9|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|15.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||9.3|21.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|16.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||12.2|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|17.5|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|20.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic|20.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other|7.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other|18.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other|24.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other|27.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||Native Hawaiian or other PI||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other|45.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||26.3|65.5
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|7.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||3.3|10.7
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|7.8|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS|||||4.9|10.7
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|9.9|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|10.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|13.8|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|15.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|16.6|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2013 NC BRFSS (Mecklenburg Sample)|||||11.2|22.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|17.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|18.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|19.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||16.2|22.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|19.7|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|20.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|20.8|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||12.0|33.7
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|21.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||17.5|24.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|21.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||16.0|26.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|23.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||19.9|27.7
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Both|White|24.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||17.8|30.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|7.7|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||3.4|11.9
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|7.9|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|8.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|10.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|11.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|12.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2013 NC BRFSS (Mecklenburg Sample)|||||7.5|16.4
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|12.5|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|14.7|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|15.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||12.8|17.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|15.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|18.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|18.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||15.4|22.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||18.0|26.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|22.9|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||18.7|27.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|23.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||17.1|30.9
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All|31.0|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||23.6|38.5
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Female|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|12.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|prevalence of current smoking, age-adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|17.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|18.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||9.2|27.5
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|18.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|20.0|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported; percent is age adjusted|||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|20.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|20.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Adult Cigarette Smoking, National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|20.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||16.4|25.1
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|21.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2013 NC BRFSS (Mecklenburg Sample)|||||15.1|27.8
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|22.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission ||This survey is not conducted annually. Data is based on 2008, 2010, and 2014 files. 2015 files are not yet available.|||19.3|25.2
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|22.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|23.4|Washington, DC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|DC BRFSS||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|24.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||19.2|29.1
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|28.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||23.0|33.0
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|28.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||20.8|36.7
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All|35.2|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||26.8|44.6
Chronic Disease|Percent of Adults Who Currently Smoke|2013|Male|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|10.7|San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS|||||4.5|17.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|10.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||8.1|14.4
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|12.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||0.1|0.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|13.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||12.8|14.9
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|14.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|14.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2014 NC BRFSS (Mecklenburg Sample)|||||10.9|18.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|15.8|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||11.6|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|15.9|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|16.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||16.3|17.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|17.0|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|used crude rates|2014 BRFSS|||14.5|19.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|17.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||14.4|19.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|17.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||14.5|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|22.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||19.2|25.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|22.3|Kansas City, MO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|23.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||20.0|27.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|26.4|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||19.6|33.3
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|All|34.4|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||28.5|40.8
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|American Indian/Alaska Native|16.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||15.5|18.1
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|American Indian/Alaska Native|28.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI|4.8|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||1.4|14.8
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI|11.2|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI|12.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||2.0|22.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI|13.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||11.0|16.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|13.4|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||4.0|22.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|14.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||12.7|17.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|16.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|17.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||15.7|18.3
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|17.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2014 NC BRFSS (Mecklenburg Sample)|||||10.3|25.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|20.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS ||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|23.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||13.8|33.3
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|25.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||15.5|34.4
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|28.0|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||21.1|34.9
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black|31.4|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||25.0|38.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Black||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Solely respresentative of American Indian population||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic|10.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||9.8|11.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic|12.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||11.1|14.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic|14.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic|14.4|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic|15.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||9.9|20.8
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic|19.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||13.8|24.8
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Hispanic||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Multiracial|26.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||21.0|31.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other|15.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||9.6|25.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other|16.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||3.9|29.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other|23.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|Other||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|9.4|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||6.6|13.3
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|12.9|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|14.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||11.0|17.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|14.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||6.8|22.3
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|14.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||12.7|17.1
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|15.2|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2014 NC BRFSS (Mecklenburg Sample)|||||9.7|20.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|16.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|18.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||14.6|21.4
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|19.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||18.2|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|22.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||18.5|26.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|23.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||18.0|28.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White|27.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||19.3|36.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Both|White||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|6.2|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||3.6|10.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|10.0|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||8.8|11.4
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|10.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|13.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2014 NC BRFSS (Mecklenburg Sample)|||||8.1|17.9
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|13.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|13.6|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|used crude rates|2014 BRFSS|||10.6|16.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|14.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||10.9|17.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|15.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||11.6|18.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|15.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||14.2|16.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|19.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||15.6|22.9
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||18.0|27.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|26.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||18.0|35.3
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All|31.5|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||24.0|40.1
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Female|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|15.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||10.7|20.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|16.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2014 NC BRFSS (Mecklenburg Sample)|||||10.6|22.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|17.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS||Value is for Bexar County, TX||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|18.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||16.5|19.9
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|18.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|National Health Interview Survey (NHIS), CDC/NCHS Table A-12a. Age-adjusted percentages (with standard errors) of current cigarette smoking status among adults aged 18 and over, by selected characteristics: United States, 2014; https://www.cdc.gov/nchs/nhis/shs/tables.htm|||||17.9|19.7
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|18.9|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Arizona BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|19.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||15.1|23.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|19.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||15.8|24.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|20.3|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||8.9|31.6
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|20.5|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|used crude rates|2014 BRFSS|||16.5|24.5
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|24.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||19.0|30.0
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|25.4|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||20.7|30.1
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|26.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||15.2|37.2
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All|38.0|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|The proportion of adults who reported that they had ever smoked at least 100 cigarettes (five packs) in their life and that they smoke cigarettes now, either every day or on some days.||||29.0|47.8
Chronic Disease|Percent of Adults Who Currently Smoke|2014|Male|All||San Francisco, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|10.3|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||6.9|13.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|10.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||8.1|14.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|11.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County. |||0.1|0.2
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|14.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||13.3|15.3
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|14.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2015 NC BRFSS (Mecklenburg Sample)|||||10.3|19.3
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|15.5|Long Beach, CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Source: 2015 Los Angeles County Health Survey.  Note: 2015 estimates are based on self-reported data by a random sample of 8,008 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|16.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||14.1|18.2
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|16.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||12.3|20.6
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|16.7|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.||||||14.1|19.2
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|16.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||13.8|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|21.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||17.9|25.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||18.0|27.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|23.2|Kansas City, MO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|All|30.8|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||23.8|37.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI|12.6|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||5.8|19.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI|14.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||12.3|17.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI|18.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||5.1|31.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Asian/PI||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|13.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||11.9|15.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|19.6|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||15.4|23.8
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|21.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2015 NC BRFSS (Mecklenburg Sample)|||||12.7|30.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|23.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||11.9|39.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|28.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||21.0|36.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|28.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||17.4|40.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|29.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||15.7|43.5
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black|32.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||13.8|51.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Black||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic|12.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||10.9|14.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic|12.7|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||8.5|16.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic|13.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||7.6|20.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic|15.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||7.4|23.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic|16.5|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||7.3|33.1
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Hispanic||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other|8.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||0.0|17.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other|19.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||5.0|34.1
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other|20.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||14.0|28.1
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|Other||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|10.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||8.5|13.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|12.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2015 NC BRFSS (Mecklenburg Sample)|||||6.2|19.1
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|15.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||10.1|20.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|15.7|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||13.7|17.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|15.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||12.2|19.6
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|16.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||12.9|19.3
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|17.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||11.0|24.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|21.9|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||17.2|26.6
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White|29.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||20.7|38.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Both|White||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|11.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||9.9|12.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|11.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||8.4|16.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|12.6|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2015 NC BRFSS (Mecklenburg Sample)|||||7.0|18.2
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|12.8|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||10.5|15.2
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|12.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||7.8|17.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|13.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||9.8|17.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|15.0|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.||||||11.6|18.3
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|21.5|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||16.3|26.8
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||17.0|29.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All|25.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||16.8|33.6
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Female|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|12.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Washington State Department of Health, Center for Health Statistics, Behavioral Risk Factor Surveillance System, |||||9.6|16.6
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|16.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|2015 NC BRFSS (Mecklenburg Sample)|||||9.4|23.5
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|17.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||16.3|19.5
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|18.5|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.||||||14.7|22.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|19.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||13.2|25.9
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|20.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||16.4|23.5
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|20.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Nevada BRFSS - Clark County|||||15.5|25.4
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|21.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||15.0|28.0
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|22.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||16.6|27.5
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All|36.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||25.4|47.7
Chronic Disease|Percent of Adults Who Currently Smoke|2015|Male|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|All|17.5|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||14.8|20.1
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|All|20.7|Kansas City, MO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|All|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||18.0|26.0
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|All|27.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||22.3|33.6
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Asian/PI||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Black|15.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||7.4|24.3
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Black|22.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||13.1|32.7
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Black|23.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||17.0|30.0
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Black||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Hispanic|21.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||16.4|27.5
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Hispanic||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Other|15.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||5.3|25.3
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|White|15.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||12.3|19.2
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|White|22.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||17.0|28.0
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|White|28.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||21.1|36.1
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Both|White||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Female|All|13.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||9.7|16.3
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Female|All|18.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||14.0|24.0
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Female|All|27.2|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||19.8|34.6
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Female|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Male|All|21.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|Colorado BRFSS|||||17.8|26.0
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Male|All|26.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|PA Eddie-->BRFSS |||||20.0|33.0
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Male|All|28.6|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|BRFSS|||||20.0|37.2
Chronic Disease|Percent of Adults Who Currently Smoke|2016|Male|All||Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that smokes using CDC definition; variable RFSMOK3 combines two questions, defining current smoker as having smoked 100 cigarettes in lifetime AND being an everyday or someday smoker.|California Health Interview Survey (AskCHIS)|Aults who smoked 100 or more in life and reported to be current smokers|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2010|Both|All|68.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Black|62.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2010|Both|White|75.5|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2010|Female|All|62.7|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2010|Male|All|75.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|19.1|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|29.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|32.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CHIS- 2005, 2009|Adults 18+ years, Moderate physical activity at least 5 days/wk and 30 min/day|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|33.7|Long Beach, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Source: 2011 Los Angeles County Health Survey.  Note: 2011 estimates are based on self-reported data by a random sample of 8,036 Los Angeles County adults, representative of the adult population in Los Angeles County.  The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|40.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||34.5|45.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|43.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||35.6|51.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|44.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||41.0|48.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|47.2|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||42.4|52.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|47.9|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|48.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|50.7|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|51.8|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2011 NC BRFSS (Mecklenburg Sample)|||||46.6|56.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|52.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|57.7|Minneapolis, MN|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS SMART County Prevalence Data|Meet aerobic recommendations; Respondents that reported doing 150+ minutes (or vigorous equivalent) of physical activity; Variable: _PAINDEX; Variable was added in 2011 and is collected every other year; However, BRFSS SMART County data for 2013 and 2014 is not yet available|County data was used as a proxy (Hennepin County)||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|61.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||57.3|66.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|68.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|74.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||71.8|77.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|American Indian/Alaska Native|42.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||American Indian alone||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|25.8|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CHIS- 2005, 2009|Adults 18+ years, Moderate physical activity at least 5 days/wk and 30 min/day|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|26.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|45.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|47.7|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||29.5|66.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|70.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||59.4|81.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|19.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|30.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|39.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||24.0|55.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|40.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|43.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||27.8|61.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|45.7|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|46.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||40.0|52.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|48.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2011 NC BRFSS (Mecklenburg Sample)|||||38.8|58.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|59.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||45.9|73.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|63.9|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|70.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||61.1|79.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|25.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|27.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|40.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|41.4|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|43.5|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||36.2|51.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|44.2|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CHIS- 2005, 2009|Adults 18+ years, Moderate physical activity at least 5 days/wk and 30 min/day|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|49.8|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|61.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||52.1|70.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other|37.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||Native Hawaiian or other PI||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other|76.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||64.1|89.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|14.9|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|33.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|34.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CHIS- 2005, 2009|Adults 18+ years, Moderate physical activity at least 5 days/wk and 30 min/day|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|36.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||30.8|43.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|44.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||39.0|49.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|45.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||36.4|54.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|52.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|53.9|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||47.4|60.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|55.8|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|57.2|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|57.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2011 NC BRFSS (Mecklenburg Sample)|||||51.3|64.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|64.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||59.4|69.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|77.5|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|79.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||76.1|82.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|25.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|33.1|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||25.9|41.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|39.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CHIS- 2005, 2009|Adults 18+ years, Moderate physical activity at least 5 days/wk and 30 min/day|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|40.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||35.0|44.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|43.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||37.6|48.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|43.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2011 NC BRFSS (Mecklenburg Sample)|||||37.0|49.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|44.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||34.7|54.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|45.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|48.7|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|50.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|64.2|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||58.7|69.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|71.1|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|73.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||69.5|77.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|24.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CHIS- 2005, 2009|Adults 18+ years, Moderate physical activity at least 5 days/wk and 30 min/day|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|33.0|Los Angeles, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Using Physical Activity Guidelines Advisory Committee Report, 2008. Wash DC: U.S. DHHS. http://www.health.gov/paguidelines/pdf/paguide             Asian does NOT include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|41.7|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||29.8|53.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|46.9|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||39.0|55.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|50.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||44.0|55.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|51.6|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||43.7|59.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|52.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|52.8|Chicago, Il|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|53.3|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2011 NC BRFSS (Mecklenburg Sample)|||||45.3|61.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|54.8|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|59.6|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||53.2|66.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|65.2|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|76.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||71.7|80.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|20.8|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||17.1|24.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|38.1|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AskCHIS Neighborhood Edition|Adults ages 18+ who walked for transportation or leisure for at least 150 minutes in the past week.||||34.2|42.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|47.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 BRFSS Bexar County|150 mins activity|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|50.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|68.6|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|78.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||75.9|80.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|American Indian/Alaska Native|46.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||American Indian alone||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Asian/PI|49.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Asian/PI|75.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||66.0|85.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Black|40.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Black|67.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Black|78.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||70.7|85.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Hispanic|17.4|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||11.1|23.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Hispanic|42.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Hispanic|73.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||69.2|78.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Other|64.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||Native Hawaiian or other PI||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Other|78.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||67.5|88.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|White|21.8|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||17.4|26.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|White|53.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|White|73.1|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Both|White|80.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||78.1|83.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Female|All|16.5|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||12.3|20.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Female|All|46.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Female|All|71.4|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Female|All|75.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||72.9|78.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Male|All|25.1|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||19.1|31.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Male|All|53.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Male|All|68.1|Baltimore, MD|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County. BRFSS question: During the past month| other than your regular job| did you participate in any physical activities or exercises such as running| calisthenics| golf| gardening| or walking for exercise? Percent responding yes.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2012|Male|All|80.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||77.0|83.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|18.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||13.3|24.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|24.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||22.2|26.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|30.4|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||27.0|33.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|38.4|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||34.4|42.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|39.4|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||34.2|44.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|40.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||37.0|44.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|44.0|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 BRFSS Bexar County|150 mins activity|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|44.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||38.8|49.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|49.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|50.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||44.0|56.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|51.2|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|51.6|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 NC BRFSS (Mecklenburg Sample)|||||46.1|57.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|53.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|56.5|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||50.8|62.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|57.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||53.6|61.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|67.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|75.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||72.9|78.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|American Indian/Alaska Native|46.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||American Indian alone||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|20.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||14.0|26.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|38.8|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||26.2|53.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|46.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|49.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||Does not include Pacific Islander||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|57.9|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|78.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||68.4|89.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|19.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||13.8|27.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|22.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||19.1|25.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|31.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||23.2|39.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|35.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||30.0|41.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|38.3|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|41.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|47.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||36.4|59.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|49.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||36.0|62.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|54.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 NC BRFSS (Mecklenburg Sample)|||||43.7|65.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|62.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|62.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||51.7|74.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|66.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|20.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||16.1|24.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|40.1|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||32.6|48.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|42.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|46.1|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||33.9|58.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|46.8|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||39.4|54.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|47.1|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|48.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|62.4|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|79.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||73.1|86.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other|64.0|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other|64.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||Native Hawaiian or other PI||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other|75.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||61.6|90.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|27.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||24.1|30.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|35.2|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||31.0|39.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|44.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||39.0|50.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|49.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||41.8|56.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|51.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||43.6|58.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|53.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|54.2|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 NC BRFSS (Mecklenburg Sample)|||||46.7|61.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|55.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|62.7|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||56.2|69.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|63.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|64.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||60.4|69.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|75.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|75.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||72.0|79.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|16.6|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||10.7|24.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|20.2|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||17.8|22.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|28.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||24.3|33.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|37.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||32.0|42.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|37.2|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||32.0|42.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|40.7|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||34.0|47.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|44.2|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 NC BRFSS (Mecklenburg Sample)|||||36.9|51.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|46.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|47.9|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|51.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||42.8|59.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|54.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||49.7|60.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|55.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|56.2|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||48.6|63.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|64.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|71.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||67.5|75.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|20.3|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||12.7|30.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|28.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|||||25.4|31.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|32.3|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||27.1|37.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|39.5|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||33.8|45.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|44.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||38.0|50.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|47.8|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||39.4|56.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|48.9|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||40.0|57.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|51.0|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2013 NC BRFSS (Mecklenburg Sample)|||||43.6|58.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|52.0|San Jose, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as meets CDC recommendations (adults ages 18 and older get at least 2 hours and 30 minutes (150 minutes) of moderate intensity aerobic activity (i.e., brisk walking) every week for good health; 1 hour and 15 minutes (75 minutes) of vigorous intensity aerobic activity (i.e., jogging or running); or an equivalent mix of moderate and vigorous intensity activity).|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|54.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Adults engaging in regular physical activity, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years), National Health Interview Survey (NHIS), CDC/NCHS||||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|54.6|Phoenix, AZ|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AZ BRFSS||All Maricopa County||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|56.9|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||48.1|65.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|59.9|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||54.5|65.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|71.0|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS) 2013. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Respondents were asked about recreational activities and active transportation; the percent that engaged in at least 150 moderate-equivalent minutes of physical activity per week was calculated from these questions; data are age adjusted to the year 2000 US standard population.|||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|80.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||75.7|84.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|All|28.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||27.8|29.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|All|39.2|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|AskCHIS Neighborhood Edition|Adults ages 18+ who walked for transportation or leisure for at least 150 minutes in the past week.||||34.2|44.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|All|69.3|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||67.9|70.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|All|76.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||73.9|79.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|American Indian/Alaska Native|20.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||14.4|25.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Asian/PI|30.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),||Asian alone|||27.7|33.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Asian/PI|64.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||60.4|69.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Asian/PI|71.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||58.4|83.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Asian/PI||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Black|23.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||21.9|25.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Black|65.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||62.6|68.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Black|70.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||60.2|79.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Black||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Hispanic|25.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||24.4|27.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Hispanic|64.2|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||61.6|66.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Hispanic|77.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||71.0|83.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Hispanic||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Multiracial|20.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||15.1|25.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Other|77.6|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||67.4|85.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Other|84.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||70.2|97.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Other||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|White|29.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||28.9|30.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|White|77.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||74.7|79.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|White|78.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||75.5|81.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Both|White||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Female|All|29.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||28.4|30.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Female|All|66.8|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||64.8|68.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Female|All|74.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||70.0|78.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Female|All||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Male|All|27.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|National Health Interview Survey (NHIS), CDC/NCHS, Table A-14a. Age-adjusted percent distributions (with standard errors) of participation in leisure-time aerobic and muscle-strengthening activities that meet the 2008 federal physical activity guidelines among adults aged 18 and over, by selected characteristics: United States, 2014, Adults who met aerobic activity guidelines, Moderate for 150+ minutes/week or vigorous for 75+ minutes/week (age adjusted, percent, 18+ years),|||||26.7|28.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Male|All|72.1|New York City, NY|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||70.0|74.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Male|All|79.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||75.4|83.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2014|Male|All||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|17.4|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||12.8|23.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|18.8|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||16.7|20.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|27.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||24.0|30.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|32.1|Long Beach, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Source: 2015 Los Angeles County Health Survey.  Note: 2015 estimates are based on self-reported data by a random sample of 8,008 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|35.9|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||31.6|40.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|44.2|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||38.4|50.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|44.6|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||38.3|50.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|48.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||43.0|54.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|49.5|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2015 NC BRFSS (Mecklenburg Sample)|||||43.5|55.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|50.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||42.9|57.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|50.9|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||47.4|54.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|59.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||53.4|64.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|60.6|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.||||||56.2|64.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|74.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||71.1|77.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|15.3|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||8.4|22.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|21.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS||Does not include PI|||12.0|33.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|50.4|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||35.8|65.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|80.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||68.9|92.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|16.7|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||11.6|23.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|19.5|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||15.5|23.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|23.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||11.0|41.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|37.3|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||27.5|47.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|40.8|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||25.6|56.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|43.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||23.7|62.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|44.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||36.0|53.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|50.7|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2015 NC BRFSS (Mecklenburg Sample)|||||40.3|61.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|72.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||61.3|82.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|10.4|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||6.7|14.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|35.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||22.0|51.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|40.9|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||35.1|46.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|41.9|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||33.6|50.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|55.1|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||42.9|67.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|72.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||64.9|80.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other|32.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS||Multiple Race|||17.0|52.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other|56.0|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS||Includes Asian/PI|||31.3|80.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other|59.4|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||44.5|74.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other|81.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||68.1|94.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other||Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|22.0|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||18.6|25.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|28.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||24.0|31.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|36.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||31.8|41.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|51.4|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2015 NC BRFSS (Mecklenburg Sample)|||||42.3|60.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|53.3|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||44.4|61.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|54.5|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||45.3|63.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|56.6|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||52.1|61.2
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|60.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||52.0|66.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|64.3|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||57.3|71.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|74.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||69.5|78.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|10.7|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||6.8|16.3
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|18.1|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||15.3|20.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|25.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||21.0|30.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|36.7|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||30.8|42.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|40.4|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||33.2|48.1
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|41.9|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2015 NC BRFSS (Mecklenburg Sample)|||||34.9|48.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|47.3|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||38.0|56.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|48.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||42.0|55.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|52.2|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||47.1|57.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|56.7|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||48.7|64.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|58.7|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.||||||52.5|64.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|69.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||65.1|74.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|19.6|Boston, MA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Boston Behavioral Risk Factor Survey, 2015, Boston Public Health Commission||This survey is conducted every other year.|||16.4|22.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|25.9|Detroit, MI|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|The proportion who reported that they do either moderate physical activities for at least 150 minutes per week, vigorous physical activities for at least 75 minutes per week, or an equivalent combination of moderate and vigorous physical activities and also participate in muscle strengthening activities on two or more days per week.||||17.2|36.9
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|28.0|Seattle, WA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||24.0|33.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|35.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percnet of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Data available only for odd years|||28.7|41.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|48.5|San Antonio, TX|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS survery data||Bexar County level data|||39.6|57.5
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|48.6|Charlotte, NC|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|2015 NC BRFSS (Mecklenburg Sample)|||||39.5|57.8
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|49.0|Philadelphia, PA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|PA Eddie-->BRFSS |||||41.0|56.0
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|49.6|San Diego County, CA|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults who meet aerobic recommendations.|2012-2013 and 2015 data available- 2014 data is not available|||44.9|54.4
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|53.1|Columbus, OH|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|BRFSS|||||41.6|64.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|61.4|Denver, CO|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Colorado BRFSS|||||53.2|69.7
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|62.5|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.||||||56.3|68.6
Chronic Disease|Percent of Adults Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|79.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of population 18 years and over that meets CDC physical activity recommendations: 18 and over get at least 2 hrs, 30 mins of moderate intensity aerobic activity every week for good health; 1 hr, 15 mins of vigorous intensity aerobic activity; or an equivalent mix of moderate and vigorous.|Nevada BRFSS - Clark County|||||74.6|84.2
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|All|9.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||7.0|11.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|All|11.6|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data|percent of students (in this high-school survey) who were obese (95th percentile BMI, by age and sex)|||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|All|15.2|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|All|15.5|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|All|20.2|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||10.8|29.7
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Asian/PI|8.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||5.0|12.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Asian/PI|9.1|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Black|14.0|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Black|17.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Black|19.8|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Hispanic|13.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Hispanic|15.6|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Hispanic|16.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||10.0|24.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Hispanic|17.3|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Other|7.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data||Other includes Asian/PI| Native Am/Alaska Native but does not include multiracial||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|Other|12.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||7.0|21.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|White|6.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||4.0|9.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|White|7.9|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Both|White|9.1|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Female|All|6.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||4.0|8.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Female|All|9.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Female|All|11.6|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Female|All|11.8|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Female|All||Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Male|All|12.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||9.0|15.0
Chronic Disease|Percent of High School Students Who Are Obese|2010|Male|All|13.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|PANT questions asked as part of CPPW grant - weighted data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Male|All|18.4|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Male|All|19.5|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2010|Male|All|31.8|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||15.7|48.0
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|7.4|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||5.8|9.4
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|11.6|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|12.7|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|12.7|San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||8.2|17.3
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|12.8|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2011  Charlotte Mecklenburg High School YRBS Survey|||||10.9|15.0
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|13.3|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|14.3|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||11.7|16.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|17.3|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All|18.9|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||17.0|20.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|California Health Interview Survey. Percent of teens who are obese (BMI highest 5th percentile)|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Asian/PI|4.6|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||3.3|6.2
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Asian/PI|8.6|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Asian/PI|10.4|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black|13.8|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black|14.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||9.4|19.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black|16.9|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2011  Charlotte Mecklenburg High School YRBS Survey|||||13.6|20.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black|18.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black|18.8|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black|18.9|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||16.9|21.1
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|11.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|12.5|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2011  Charlotte Mecklenburg High School YRBS Survey|||||8.3|18.5
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|14.1|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|14.7|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||9.3|22.5
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|14.8|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||10.1|21.0
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|15.3|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|16.6|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||12.3|20.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|19.5|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Hispanic|22.0|San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||12.8|31.3
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Other|10.2|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White|6.1|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||3.3|10.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White|6.5|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2011  Charlotte Mecklenburg High School YRBS Survey|||||4.6|9.3
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White|6.5|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White|8.6|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White|8.9|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White|12.9|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||6.9|18.8
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Both|White||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|6.1|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||4.1|8.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|6.3|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|9.1|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|9.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|14.2|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||10.6|17.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|15.0|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All|19.0|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||16.6|21.7
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Female|All||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|8.8|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||6.9|11.2
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|13.6|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2011  Charlotte Mecklenburg High School YRBS Survey|||||11.0|16.8
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|14.1|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|14.4|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||11.1|17.7
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|16.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|18.7|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||16.1|21.7
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|19.2|San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||11.4|26.9
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|19.7|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All|19.8|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2011|Male|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|All|7.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||6.0|9.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|All|24.3|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||18.1|30.6
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|Asian/PI|7.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||4.0|12.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|Black|9.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||5.0|14.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|Hispanic|12.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||7.0|21.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|Hispanic||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|Other||Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey ||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, there are too few cases to report reliable rates.||||
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|White|5.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||3.0|7.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Both|White||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2012|Female|All|4.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||3.0|6.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Female|All|28.7|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||19.2|38.2
Chronic Disease|Percent of High School Students Who Are Obese|2012|Male|All|10.0|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Healthy Youth Survey |||||8.0|13.0
Chronic Disease|Percent of High School Students Who Are Obese|2012|Male|All|20.2|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||12.2|28.2
Chronic Disease|Percent of High School Students Who Are Obese|2012|Male|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|7.7|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||6.2|9.6
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|9.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|10.7|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|11.1|San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)/ 2013-15 (Pooled)|||4.9|17.3
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|11.8|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2013  Charlotte Mecklenburg High School YRBS Survey|||||9.7|14.3
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|11.8|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|12.1|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||10.2|14.0
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|13.6|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|13.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2013, https://nccd.cdc.gov/youthonline/App/QuestionsOrLocations.aspx?CategoryId=C7||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|13.8|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||11.4|16.2
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|14.4|San Antonio, TX|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|14.5|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|14.6|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|14.8|Washington, DC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|DC YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|20.0|San Jose, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Defined as overweight for age|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All|22.9|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI|4.8|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||3.4|6.6
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI|5.3|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI|5.5|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI|6.2|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI|8.1|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|11.1|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|11.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|13.7|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|14.0|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2013  Charlotte Mecklenburg High School YRBS Survey|||||11.0|17.7
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|14.0|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|14.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||10.9|18.6
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|15.3|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|15.7|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|15.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2013, https://nccd.cdc.gov/youthonline/App/QuestionsOrLocations.aspx?CategoryId=C7||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black|22.6|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|9.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|13.3|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|13.8|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2013  Charlotte Mecklenburg High School YRBS Survey|||||8.8|20.9
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|13.9|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|14.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||11.3|17.6
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|14.6|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|14.9|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||11.3|19.5
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|15.2|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|15.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2013, https://nccd.cdc.gov/youthonline/App/QuestionsOrLocations.aspx?CategoryId=C7||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|15.9|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|16.0|San Antonio, TX|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|18.0|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||14.0|22.1
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|26.0|San Jose, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Defined as overweight for age|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic|29.2|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Hispanic||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Multiracial|13.3|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Other|16.2|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|3.6|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||1.2|10.4
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|3.9|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|5.7|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|6.1|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|6.6|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|6.8|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2013  Charlotte Mecklenburg High School YRBS Survey|||||4.5|10.2
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|6.9|San Antonio, TX|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|7.4|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|7.9|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||4.9|10.9
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|8.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||3.5|13.8
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|13.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2013, https://nccd.cdc.gov/youthonline/App/QuestionsOrLocations.aspx?CategoryId=C7||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White|13.6|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Both|White||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|5.1|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||3.6|7.0
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|5.9|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|7.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||5.6|9.9
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|8.6|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|9.6|San Antonio, TX|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|10.0|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2013  Charlotte Mecklenburg High School YRBS Survey|||||7.4|13.3
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|10.3|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|10.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2013, https://nccd.cdc.gov/youthonline/App/QuestionsOrLocations.aspx?CategoryId=C7||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|11.3|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|12.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||9.3|16.1
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|12.8|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|13.5|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|16.0|San Jose, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Defined as overweight for age|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All|20.8|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Female|All||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|10.3|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||8.0|13.2
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|13.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|13.2|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Based on self-reported height and weight; Only  high school aged children|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|13.4|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|13.6|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2013  Charlotte Mecklenburg High School YRBS Survey|||||10.7|17.3
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|14.8|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||11.3|18.4
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|15.7|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|16.3|Chicago, Il|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|16.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2013, https://nccd.cdc.gov/youthonline/App/QuestionsOrLocations.aspx?CategoryId=C7||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|16.7|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||13.5|19.8
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|18.1|Los Angeles, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS||Adolescents||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|19.0|San Antonio, TX|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|24.0|San Jose, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Defined as overweight for age|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All|25.4|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2013|Male|All||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2013-2015, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|All|0.2|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|PA=participated in PA for 60+ minutes on 7 or more of the past 7 days||||0.2|0.2
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|All||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Asian/PI|0.2|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014||Representative of Asiain population alone. Does not include Pacific Islander population|||0.1|0.2
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Black|0.2|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|||||0.2|0.3
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Hispanic|0.2|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|||||0.1|0.2
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Hispanic||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Hispanic||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Other|0.1|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|||||0.1|0.1
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|White|0.2|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|||||0.2|0.2
Chronic Disease|Percent of High School Students Who Are Obese|2014|Both|White||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Female|All|0.1|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|||||0.0|0.1
Chronic Disease|Percent of High School Students Who Are Obese|2014|Female|All||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Male|All|0.2|Seattle, WA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|WA State Healthy Youth Survey 2014|||||0.2|0.3
Chronic Disease|Percent of High School Students Who Are Obese|2014|Male|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2014|Male|All||San Diego County, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|This variable uses CDC 2010 recommendations to assign body mass index. It is created using age and gender specific BMI percentiles.  Obese is defined as highest 5th percentile for BMI. The population sampled are teens, ages 12-17. Data was pooled for 2014-2016, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|9.9|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||8.4|11.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|11.4|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||9.7|13.1
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|12.4|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||11.3|13.7
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|12.5|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||10.7|14.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|13.6|Portland (Multnomah County), OR|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Oregon Healthy Teens|Weighted % of 11th graders who are obese|2015 OHT|||11.2|16.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|13.7|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|13.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||12.5|15.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|14.5|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.||||||9.4|19.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|14.6|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||12.4|16.7
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|22.5|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||20.1|25.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All|28.9|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||23.2|34.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|American Indian/Alaska Native|15.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||7.6|30.3
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI|5.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline||Asian alone|||3.6|8.4
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI|5.6|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||3.6|8.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI|6.3|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||4.1|9.7
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI|6.6|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI|8.4|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||4.0|16.8
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI|9.6|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||5.8|13.4
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Asian/PI||Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to sample size <50. Results not availale for  2010, 2011, 2012, 2014, 2016.||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|12.6|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. ||||||8.0|17.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|13.1|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||8.8|17.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|14.1|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||11.9|16.7
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|15.3|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||12.4|18.8
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|16.3|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||8.9|28.1
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|16.4|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|16.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||14.2|19.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|17.1|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||13.3|20.9
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black|22.9|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||20.5|25.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Black||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|10.3|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|14.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||12.1|16.8
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|15.4|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||13.6|17.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|15.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||12.1|19.3
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|15.8|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||9.4|25.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|16.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||14.8|18.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|17.8|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |||Categorized as Hispanic Only or Hispanic White.|||12.7|23.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|17.9|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||13.7|23.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic|17.9|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||14.5|22.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Hispanic||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Multiracial|17.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||13.5|22.4
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Other|11.3|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||7.7|16.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Other|18.0|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||7.4|28.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|Other|21.2|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |||Categorized as Multiple Race or Hispanic Other Race.|||10.4|32.1
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|4.2|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. ||||||0.0|9.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|7.1|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||5.2|9.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|8.3|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||5.5|11.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|8.5|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||4.0|17.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|8.8|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||7.2|10.7
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|9.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||5.6|13.8
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|12.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||10.5|14.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White|14.3|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Both|White||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|5.9|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||4.3|8.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|9.2|Portland (Multnomah County), OR|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Oregon Healthy Teens|Weighted % of 11th graders who are obese|2015 OHT|||6.5|11.8
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|10.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||8.1|12.3
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|10.6|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||9.0|12.4
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|10.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||9.3|12.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|11.2|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||9.1|13.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|11.3|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. ||||||7.3|15.3
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|11.7|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||9.2|14.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|13.6|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|21.2|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||18.4|24.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Female|All|27.0|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||19.3|34.8
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|12.6|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Nevada YRBS Clark County|||||10.2|15.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|13.6|San Francisco, CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|||||11.4|16.2
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|13.8|Charlotte, NC|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |2015  Charlotte Mecklenburg High School YRBS Survey|||||11.3|16.7
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|13.9|Philadelphia, PA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |YRBS||||||
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|14.2|New York City, NY|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||12.6|15.9
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|16.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|High School YRBSS, 2015, https://nccd.cdc.gov/youthonline|||||14.8|19.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|17.2|Boston, MA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Youth Risk Behavior Survey, Centers for Disease Control and Prevention and Boston Public Schools|||||14.0|20.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|17.4|Denver, CO|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. ||||||10.4|24.3
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|18.6|Portland (Multnomah County), OR|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|Oregon Healthy Teens|Weighted % of 11th graders who are obese|2015 OHT|||15.6|21.6
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|24.2|Detroit, MI|YRBS/YRBSS (or similar summary). Percent of high school students who are obese.|YRBS|Had obesity - >= 95th percentile for body mass index, based on sex-and age-specific reference data from the 2000 CDC growth charts||||20.7|28.0
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All|30.5|Columbus, OH|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |Ohio Medicaid Assessment Survey (http://grc.osu.edu/OMAS)||Includes all of Franklin County, not just Columbus|||22.4|38.5
Chronic Disease|Percent of High School Students Who Are Obese|2015|Male|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2016|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2016|Both|Black||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2016|Both|Hispanic||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2016|Both|White||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Are Obese|2016|Male|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar summary). Percent of high school students who are obese. |California Health Interview Survey (AskCHIS)|Obese (BMI highest 5th percentile) Teen only|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|All|12.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|All|13.6|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|American Indian/Alaska Native|29.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||American Indian alone; Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Asian/PI|7.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Does not include Pacific Islander. Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Black|7.6|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Black|9.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Hispanic|16.7|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Hispanic|18.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Multiracial|17.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|Other|16.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Both|White|13.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Female|All|10.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Female|All|12.0|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Male|All|14.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2010|Male|All|15.5|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|4.8|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|8.5|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|9.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|10.0|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is conducted every other year.  Percent estimate is in response to public high school students who smoked cigarettes on 1+ days during past 30 days|||8.0|12.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|10.7|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||9.1|12.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|10.8|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|12.3|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|14.2|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||11.7|17.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All|18.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|All||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.; YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|American Indian/Alaska Native|23.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||American Indian alone||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Asian/PI|3.1|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Asian/PI|5.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Asian/PI|6.5|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Asian/PI|6.5|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||5.0|8.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Asian/PI|9.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Does not include Pacific Islander||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|4.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|4.8|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is conducted every other year.  Percent estimate is in response to public high school students who smoked cigarettes on 1+ days during past 30 days|||2.7|7.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|5.2|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|6.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|7.2|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|7.7|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|10.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black|11.0|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||8.3|14.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|7.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|10.0|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is conducted every other year.  Percent estimate is in response to public high school students who smoked cigarettes on 1+ days during past 30 days|||6.6|13.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|10.3|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|11.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|12.8|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|14.3|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|16.3|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||12.3|21.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|17.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Hispanic|17.7|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||12.8|24.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Other|7.0|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Other|8.5|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Other|14.4|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||8.3|23.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|Other|23.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Native Hawaiian or other PI||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|6.0|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|16.4|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|16.9|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|17.0|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||13.0|22.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|19.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|19.7|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||13.1|28.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Both|White|20.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|3.2|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|5.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|7.7|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|7.9|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|8.4|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is conducted every other year.  Percent estimate is in response to public high school students who smoked cigarettes on 1+ days during past 30 days|||5.6|11.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|8.4|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||6.4|11.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|9.2|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|10.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|11.5|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||9.0|14.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Female|All|16.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|6.1|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|7.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Healthy Kids Survey, http://www.chks.wested.org/ (accessed 1/2018)|Percentage of public school students in grades 7, 9, 11, and non-traditional students reporting the number of days in which they smoked cigarettes in the past 30 days, by gender; Data was pooled for 2011-2013, as single year estimates were statistically unstable.|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|9.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|10.8|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|10.9|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|11.6|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is conducted every other year.  Percent estimate is in response to public high school students who smoked cigarettes on 1+ days during past 30 days|||7.6|15.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|12.2|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||10.0|14.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|15.1|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|16.2|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2011  Charlotte Mecklenburg High School YRBS Survey|||||12.6|20.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2011|Male|All|19.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|All|1.8|Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Health Interview Survey (AskCHIS)||Current smoker - Teen.  CHIS notes rate is unstable due to small numbers.|||0.0|4.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|All|12.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|American Indian/Alaska Native|22.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||American Indian alone; Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|Asian/PI|3.4|Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Health Interview Survey (AskCHIS)||Current smoker - Teen.  CHIS notes rate is unstable due to small numbers.|||0.0|11.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|Asian/PI||Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|Black|3.4|Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Health Interview Survey (AskCHIS)||Current smoker - Teen.  CHIS notes rate is unstable due to small numbers.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|Black|4.8|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||2.7|7.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|Hispanic|10.0|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||6.6|13.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|Multiracial|16.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|White|11.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Both|White||Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Female|All|10.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2012|Male|All|13.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Current smoker, Washington State Healthy Youth Survey||Note these data are among middle and high school students, specifically grades 8,10, and 12, which makes them vary slightly from other cities' populations inclued in this data set.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|3.4|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|4.5|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||3.2|5.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|5.5|Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|California Health Interview Survey (AskCHIS)||Current smoker - Teen.  CHIS notes rate is unstable due to small numbers.|||0.0|14.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|6.7|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Los Angeles Unified School District data only. Data from the 2013 LAUSD YRBS data|Current smoking (one or more cigarettes in the past 30 days) completed by 1,208 students in 45 LAUSD public high schools in the spring of 2013.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|7.0|Baltimore, MD|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBSS for location local Baltimore, MD|Based on question Currently Smoked Cigarettes (on at least 1 day during the 30 days before the survey)|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|7.5|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|7.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|7.5|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||6.0|9.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|7.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||6.2|9.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|8.2|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|9.7|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013  Charlotte Mecklenburg High School YRBS Survey|||||7.7|12.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|10.7|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|11.0|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|11.3|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|11.3|San Antonio, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013 Bexar County YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|15.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|All|15.7|Washington, DC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|DC YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|American Indian/Alaska Native|17.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||American Indian alone||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI|4.0|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||3.0|5.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI|5.7|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Los Angeles Unified School District data only. Data from the 2013 LAUSD YRBS data|Current smoking (one or more cigarettes in the past 30 days) completed by 1,208 students in 45 LAUSD public high schools in the spring of 2013.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI|5.8|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI|7.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI|7.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI|10.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Does not include Pacific Islander||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Asian/PI||Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|2.8|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|3.1|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||1.4|4.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|4.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|5.5|Baltimore, MD|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBSS for location local Baltimore, MD|Based on question Currently Smoked Cigarettes (on at least 1 day during the 30 days before the survey)|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|5.5|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|5.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|6.7|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|7.6|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013  Charlotte Mecklenburg High School YRBS Survey|||||5.1|11.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|8.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black|8.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|5.3|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||3.3|7.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|7.0|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Los Angeles Unified School District data only. Data from the 2013 LAUSD YRBS data|Current smoking (one or more cigarettes in the past 30 days) completed by 1,208 students in 45 LAUSD public high schools in the spring of 2013.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|8.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||5.5|10.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|8.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|8.6|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||5.1|14.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|9.1|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|9.5|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|11.3|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013  Charlotte Mecklenburg High School YRBS Survey|||||7.1|17.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|11.3|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|12.1|San Antonio, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013 Bexar County YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|12.5|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|13.1|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Hispanic|14.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Multiracial|14.4|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Other|9.8|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|Other|11.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Native Hawaiian or other PI||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|5.8|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||2.2|9.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|8.9|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||5.8|12.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|9.4|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|11.4|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013  Charlotte Mecklenburg High School YRBS Survey|||||8.3|15.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|11.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|13.1|San Antonio, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013 Bexar County YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|15.2|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|15.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|18.4|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Both|White|19.2|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||10.8|31.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|2.8|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|3.0|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||1.8|4.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|4.9|Baltimore, MD|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBSS for location local Baltimore, MD|Based on question Currently Smoked Cigarettes (on at least 1 day during the 30 days before the survey)|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|6.2|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||4.2|9.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|6.7|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Los Angeles Unified School District data only. Data from the 2013 LAUSD YRBS data|Current smoking (one or more cigarettes in the past 30 days) completed by 1,208 students in 45 LAUSD public high schools in the spring of 2013.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|6.9|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|7.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|7.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|7.5|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|7.8|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|7.8|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|8.2|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013  Charlotte Mecklenburg High School YRBS Survey|||||6.0|11.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|8.4|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||6.1|10.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|9.9|San Antonio, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013 Bexar County YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Female|All|15.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|3.7|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|5.7|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survellience System|||||3.6|7.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|6.8|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Los Angeles Unified School District data only. Data from the 2013 LAUSD YRBS data|Current smoking (one or more cigarettes in the past 30 days) completed by 1,208 students in 45 LAUSD public high schools in the spring of 2013.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|7.3|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||5.0|9.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|7.8|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|8.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|8.4|Baltimore, MD|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBSS for location local Baltimore, MD|Based on question Currently Smoked Cigarettes (on at least 1 day during the 30 days before the survey)|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|8.6|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||6.7|11.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|9.2|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|11.0|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013  Charlotte Mecklenburg High School YRBS Survey|||||8.3|14.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|12.6|San Antonio, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2013 Bexar County YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|14.0|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|14.3|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS]|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|14.9|Houston, TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Currently smoked cigarettes, Houston TX HS Youth Risk Behavior Survey, Youth Online:  High School YRBS||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2013|Male|All|16.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 912), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Both|All|10.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey |||||9.0|13.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Both|Asian/PI|5.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||4.0|8.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Both|Hispanic|12.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey |||||8.0|17.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Both|Other|17.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey |||||11.0|26.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Both|White|10.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey |||||8.0|14.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Female|All|10.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey |||||8.0|13.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2014|Male|All|11.0|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Healthy Youth Survey |||||9.0|13.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|4.8|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey,  2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||3.5|6.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|5.4|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||4.0|7.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|5.8|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||4.7|7.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|5.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.||||||3.6|8.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|5.9|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||4.7|7.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|7.0|Portland (Multnomah County), OR|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Oregon Healthy Teens|Weighted % of 11th graders who are current smokers|2015 OHT:During the past 30 days, did you smoke cigarettes? tbcigcomb|||5.7|8.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|7.2|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||5.6|9.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|8.6|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|Currently smoked cigarettes - on at least 1 day during the 30 days before the survey||||5.6|12.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|9.0|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||7.5|10.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|10.3|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||8.6|12.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|All|10.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||9.4|12.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|American Indian/Alaska Native|12.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||7.1|20.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Asian/PI|2.2|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||1.2|3.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Asian/PI|2.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||1.2|5.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Asian/PI|4.8|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||3.5|6.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Asian/PI|6.4|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||2.8|13.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Asian/PI|7.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Asian alone|||4.2|11.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Asian/PI||Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to sample size <50. Results not availale for  2010, 2011, 2012, 2014, 2016.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|0.6|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.||||||0.0|1.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|3.2|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey,  2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||1.6|4.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|3.3|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||1.9|5.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|3.3|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||2.2|4.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|3.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||2.4|5.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|4.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||1.5|6.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|6.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||4.8|8.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|8.8|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|Currently smoked cigarettes - on at least 1 day during the 30 days before the survey||||5.6|13.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black|8.9|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||6.5|12.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|4.4|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|Currently smoked cigarettes - on at least 1 day during the 30 days before the survey||||2.0|9.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|5.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||Categorized as Hispanic Only or Hispanic White.|||1.3|9.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|5.3|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||4.1|6.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|5.8|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey,  2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||3.5|8.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|6.7|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||4.9|8.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|6.9|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||4.8|9.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|8.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||4.3|16.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|9.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||7.9|10.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Hispanic|10.7|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||7.8|14.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Multiracial|14.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||11.1|18.0
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Other|7.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||4.7|10.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Other|15.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|||Categorized as Multiple Race or Hispanic Other Race.|||4.8|25.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|Other||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; Fort Worth Independent School District (not all of Tarrant County)||||
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|5.3|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey,  2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||1.7|8.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|6.4|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||4.3|8.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|7.5|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.||||||2.8|12.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|11.0|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||8.3|14.5
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|12.6|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||9.4|16.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|14.3|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||10.5|19.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|14.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||9.0|22.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Both|White|15.9|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||9.8|24.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|3.4|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey,  2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||2.2|4.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|4.3|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.||||||1.2|7.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|4.7|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||3.9|5.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|5.0|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||3.3|7.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|5.9|Portland (Multnomah County), OR|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Oregon Healthy Teens|Weighted % of 11th graders who are current smokers|2015 OHT:During the past 30 days, did you smoke cigarettes? tbcigcomb|||4.2|7.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|6.4|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||4.7|8.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|6.5|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||5.1|8.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|6.8|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||4.8|9.6
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|7.1|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||5.5|9.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|8.7|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|Currently smoked cigarettes - on at least 1 day during the 30 days before the survey||||5.3|13.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Female|All|9.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||8.1|11.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|5.3|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Nevada YRBS Clark County|||||3.9|6.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|5.6|San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||3.7|8.3
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|6.0|Boston, MA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Youth Risk Behavior Survey,  2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||3.7|8.2
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|6.6|New York City, NY|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||5.1|8.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|7.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|||||5.1|9.8
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|7.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.||||||3.7|10.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|8.1|Portland (Multnomah County), OR|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Oregon Healthy Teens|Weighted % of 11th graders who are current smokers|2015 OHT:During the past 30 days, did you smoke cigarettes? tbcigcomb|||6.1|10.1
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|8.5|Detroit, MI|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|YRBS|Currently smoked cigarettes - on at least 1 day during the 30 days before the survey||||5.5|12.9
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|10.9|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||8.7|13.7
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|11.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|Adolescent cigarette smoking in past month (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||10.4|13.4
Chronic Disease|Percent of High School Students Who Currently Smoke|2015|Male|All|13.5|Charlotte, NC|YRBS/YRBSS (or similar survey). Percent of high school students that smoked cigarettes on at least 1 day during the 30 days before the survey.|2015  Charlotte Mecklenburg High School YRBS Survey|||||10.8|16.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|All|15.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||14.0|17.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|All|18.2|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|All|21.3|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|All|22.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data|% of students who were physically active: defined as those who were physically active for a total of at least 60 minutes per day on 7 of the last seven days.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Asian/PI|11.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||9.0|15.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Asian/PI|20.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Black|18.3|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Black|19.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||16.0|23.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Black|24.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Black|24.4|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Hispanic|10.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||6.0|16.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Hispanic|21.3|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Hispanic|22.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|Other|13.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data||Other includes Asian/PI| Native Am/Alaska Native but does not include multiracial||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|White|19.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||15.0|22.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|White|22.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Both|White|23.4|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Female|All|9.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||8.0|11.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Female|All|13.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Female|All|14.7|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Female|All|15.7|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Male|All|21.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||19.0|24.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Male|All|22.4|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Male|All|28.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|PANT questions asked as part of YRBS survey (funded by CPPW grant) - weighted data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2010|Male|All|28.7|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|13.4|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|15.1|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||12.9|17.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|15.2|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is not conducted annually.  Percent estimate is based on public high school students who engaged in regular physical activity 7 days out of 7 days.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|19.9|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|20.3|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|22.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|23.4|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||21.1|25.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|28.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All|37.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|California Health Interview Survey (AskCHIS)|Percent of 15-19 year olds who are physically active at least one hour every day.|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|American Indian/Alaska Native|32.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||American Indian alone||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|American Indian/Alaska Native|36.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||American Indian alone||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|12.1|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|16.0|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|16.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||Does not include Pacific Islander||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|16.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|20.1|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Asian/PI|22.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Does not include Pacific Islander||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|15.3|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||13.1|17.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|15.6|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is not conducted annually.  Percent estimate is based on public high school students who engaged in regular physical activity 7 days out of 7 days.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|15.9|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|20.7|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|20.8|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||17.9|24.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|21.0|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|21.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|26.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black|34.7|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|14.1|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|15.1|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is not conducted annually.  Percent estimate is based on public high school students who engaged in regular physical activity 7 days out of 7 days.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|17.6|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||13.3|23.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|18.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|19.2|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||12.8|27.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|19.4|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|20.4|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|21.0|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|26.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Hispanic|37.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Multiracial|23.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other|13.9|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||7.9|23.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other|22.9|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other|23.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|Other|28.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Native Hawaiian or other PI||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|16.4|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is not conducted annually.  Percent estimate is based on public high school students who engaged in regular physical activity 7 days out of 7 days.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|18.3|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|20.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|24.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|24.3|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|24.7|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|29.8|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||24.7|35.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|30.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Both|White|40.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|9.4|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is not conducted annually.  Percent estimate is based on public high school students who engaged in regular physical activity 7 days out of 7 days.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|9.4|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|13.0|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||10.6|15.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|15.0|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|16.2|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||14.0|18.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|17.6|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|18.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Female|All|27.0|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|17.4|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||14.3|21.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|17.8|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|21.2|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, Centers for Disease Control and Prevention||This survey is not conducted annually.  Percent estimate is based on public high school students who engaged in regular physical activity 7 days out of 7 days.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|25.5|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|26.0|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|26.4|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|27.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|30.9|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2011  Charlotte Mecklenburg High School YRBS Survey|||||27.4|34.6
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|38.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2011|Male|All|47.3|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|All|23.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|American Indian/Alaska Native|18.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||American Indian alone||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Asian/PI|17.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||Does not include Pacific Islander||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Black|24.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Hispanic|20.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Multiracial|26.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|Other|24.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Both|White|25.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2012|Male|All|28.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|13.3|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|15.9|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||13.8|18.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|16.4|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|16.4|Washington, DC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|DC YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|18.7|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|19.6|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|20.2|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS] 7/7 days physically active for at least 60 mins|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|22.4|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||19.9|24.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|22.5|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|25.8|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013  Charlotte Mecklenburg High School YRBS Survey|||||22.8|29.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|26.9|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|27.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|29.0|San Jose, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as physically active for at least 60 minutes a day in past 7 days (ages 5-11)|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|31.6|San Antonio, TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All|40.5|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|California Health Interview Survey (AskCHIS)|Percent of 15-19 year olds who are physically active at least one hour every day.|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|American Indian/Alaska Native|30.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||American Indian alone||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|5.7|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||2.6|8.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|19.0|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|19.0|San Jose, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as physically active for at least 60 minutes a day in past 7 days (ages 5-11)|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|19.4|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Asian/PI|21.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Does not include Pacific Islander||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|13.9|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|17.8|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||13.6|21.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|19.9|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|21.0|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|21.4|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|22.1|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|23.0|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013  Charlotte Mecklenburg High School YRBS Survey|||||19.3|27.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|24.6|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS] 7/7 days physically active for at least 60 mins|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|26.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|29.3|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||20.7|37.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black|33.7|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|8.6|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|15.7|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||12.4|19.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|16.2|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|17.7|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|18.7|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|18.7|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS] 7/7 days physically active for at least 60 mins|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|20.6|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||16.9|24.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|21.6|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|23.2|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|25.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|26.3|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013  Charlotte Mecklenburg High School YRBS Survey|||||21.1|32.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|28.3|San Antonio, TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Hispanic|42.2|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Multiracial|26.6|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Multiracial|37.5|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS] 7/7 days physically active for at least 60 mins|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other|21.6|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|Other|23.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Native Hawaiian or other PI||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|15.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|19.7|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|21.9|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|22.3|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||15.2|29.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|22.4|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS] 7/7 days physically active for at least 60 mins|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|22.4|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|23.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||19.1|28.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|27.0|San Antonio, TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|27.1|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|28.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|29.9|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013  Charlotte Mecklenburg High School YRBS Survey|||||25.0|35.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|45.2|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Both|White|51.0|San Jose, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as physically active for at least 60 minutes a day in past 7 days (ages 5-11)|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|9.6|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||7.6|11.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|10.8|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|11.3|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|13.5|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS) 2009.  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|Self-reported; defined as  physically active at least 60 minutes every day)|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|14.5|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|15.5|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|16.1|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|16.7|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013  Charlotte Mecklenburg High School YRBS Survey|||||13.8|19.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|17.7|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||14.6|20.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|17.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|23.0|San Jose, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as physically active for at least 60 minutes a day in past 7 days (ages 5-11)|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|26.9|San Antonio, TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Female|All|28.6|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|16.6|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBSS for Detroit|Two-stage, cluster sample design to produce a representative sample of students 9th-12th grade|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|21.4|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|21.9|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survellience System||This survey is not conducted annually.  Percent estimate is based on youth who met CDC recommendations for aerobics and muscle building combined.  Adopted CDC's 2013 definition for adult physical activity which differs from previous years.|||18.6|25.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|25.4|Chicago, Il|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|26.9|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|27.5|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||23.7|31.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|28.8|Los Angeles, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Updated BCHC|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|29.3|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Kids Colorado Survey (HKCS) [Colorado version of YRBS] 7/7 days physically active for at least 60 mins|These data are collected in a complex sample and weighted to be representative of the public high school population in Denver County.|||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|34.8|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013  Charlotte Mecklenburg High School YRBS Survey|||||30.7|39.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|36.0|San Antonio, TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2013 BRFSS Bexar County||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|36.0|San Jose, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Physical activity defined as physically active for at least 60 minutes a day in past 7 days (ages 5-11)|Santa Clara County level data||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|36.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2013|Male|All|52.5|Miami (Miami-Dade County), FL|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Data source Youth Risk Behavior Surveillance System (YRBSS) data||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Both|All|17.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||16.0|19.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|California Health Interview Survey (AskCHIS)|Percent of 15-19 year olds who are physically active at least one hour every day.|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Asian/PI|12.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey ||Does not include Pacific Islanders as we report data separately for this group |||9.0|15.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Black|20.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||15.0|26.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Hispanic|14.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||9.0|20.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Both|Other|15.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||8.0|26.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Female|All|13.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||11.0|15.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2014|Male|All|21.0|Seattle, WA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Healthy Youth Survey |||||19.0|24.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|15.6|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|15.8|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||13.5|18.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|18.6|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|19.7|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||27.4|12.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|20.9|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||19.4|22.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|21.2|Portland (Multnomah County), OR|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Oregon Healthy Teens|Weighted % of 11th graders who were physically active for total of 60+ min on each of past 7 days|2015 OHT|||19.3|23.2
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|24.5|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||22.5|26.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|25.6|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.||||||21.2|30.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|27.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||25.4|27.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|30.1|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||27.4|32.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All|66.6|San Diego County, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.||Age Category is 15-17 year olds. YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|California Health Interview Survey (AskCHIS)|Percent of 15-19 year olds who are physically active at least one hour every day.|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|American Indian/Alaska Native|39.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||26.1|54.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|10.8|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||5.6|19.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|12.3|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|16.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP||Asian alone|||12.5|21.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|17.0|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|18.8|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||13.0|24.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI|19.9|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||15.2|25.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI||Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to sample size <50. Results not availale for  2010, 2011, 2012, 2014, 2016.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; Fort Worth Independent School District (not all of Tarrant County)||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|16.2|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||13.8|19.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|17.8|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||14.6|21.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|18.9|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|24.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||20.4|28.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|24.6|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||21.9|27.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|25.2|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||21.1|29.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|31.7|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||26.7|36.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|31.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||25.1|38.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black|34.1|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.||||||23.1|45.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|12.9|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||7.4|21.6
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|18.2|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||14.0|23.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|18.8|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||16.6|21.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|19.6|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|20.5|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||Categorized as Hispanic Only or Hispanic White.|||14.0|26.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|24.0|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||21.4|26.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|24.4|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||21.7|27.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|24.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||22.1|27.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|25.0|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Hispanic|30.1|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||25.7|34.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Multiracial|30.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||26.6|34.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other|19.9|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||13.9|27.6
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other|35.9|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|||Categorized as Multiple Race or Hispanic Other Race.|||28.1|43.6
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|Other||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; Fort Worth Independent School District (not all of Tarrant County)||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|19.1|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|19.3|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|19.6|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||16.3|23.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|24.6|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||20.7|28.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|27.7|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.||||||25.0|30.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|29.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||26.6|31.6
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|31.2|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||26.8|35.6
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White|38.7|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||30.3|47.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Both|White||Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.; Fort Worth Independent School District (not all of Tarrant County)||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|12.2|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||10.4|14.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|12.9|Portland (Multnomah County), OR|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Oregon Healthy Teens|Weighted % of 11th graders who were physically active for total of 60+ min on each of past 7 days|OHT spreadsheet, Exercise_Nutrition_Weight tab|||10.8|15.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|14.4|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|14.5|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|14.8|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||12.3|17.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|15.9|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||13.7|18.4
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|16.5|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||14.4|18.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|17.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||16.2|19.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|19.6|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.||||||12.9|26.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|19.8|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||17.3|22.3
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Female|All|22.0|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||19.0|25.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|17.0|Detroit, MI|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS|Were physically active at least 60 minutes per day on all 7 days - doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey||||13.6|21.1
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|22.9|San Francisco, CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|25.2|Philadelphia, PA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|YRBS||||||
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|26.2|New York City, NY|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|NYC Youth Risk Behavior Survey (YRBS).  The NYC YRBS is a self-administered, anonymous survey conducted by the NYC Departments of Education and Health and Mental Hygiene in NYC public high schools.  Data are weighted to be representative of public high school students in grades 9 through 12, excluding students in juvenile detention centers, and alternative and special education schools. English as a Second Language (ESL) and special education classes in eligible high schools are also excluded from the sample.|||||23.7|28.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|27.4|Charlotte, NC|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|2015  Charlotte Mecklenburg High School YRBS Survey|||||24.1|31.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|29.4|Portland (Multnomah County), OR|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Oregon Healthy Teens|Weighted % of 11th graders who were physically active for total of 60+ min on each of past 7 days|OHT spreadsheet, Exercise_Nutrition_Weight tab|||27.1|31.0
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|31.9|Denver, CO|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.||||||26.8|36.9
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|32.5|Fort Worth (Tarrant County), TX|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Fort Worth Independent School District, 2015 Youth Risk Behavior Survey||Fort Worth Independent School District (not all of Tarrant County)|||29.4|35.8
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|34.0|Las Vegas (Clark County), NV|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Nevada YRBS Clark County|||||30.5|37.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|36.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Adolescents meeting Federal physical activity guidelines (percent, grades 9-12), Youth Risk Behavior Surveillance System (YRBSS), CDC/NCHHSTP|||||33.6|38.5
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2015|Male|All|38.0|Boston, MA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|Youth Risk Behavior Survey, 2015, Centers for Disease Control and Prevention and Boston Public Schools||This survey is conducted every other year.|||34.2|41.7
Chronic Disease|Percent of High School Students Who Meet CDC-Recommended Physical Activity Levels|2016|Both|All||Oakland (Alameda County), CA|YRBS/YRBSS (or similar survey) preferred. Percent of high school students who are active 60 minutes every day.|California Health Interview Survey (AskCHIS)|Percent of 15-19 year olds who are physically active at least one hour every day.|Data is for Alameda County; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here because CHIS reports rate is statistically unstable.||||
Demographics|Percent Foreign Born|2012|Both|All|4.3|Cleveland, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Foreign Born|2012|Both|All|5.0|Detroit, MI|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Foreign Born|2012|Both|All|6.6|Kansas City, MO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Foreign Born|2012|Both|All|7.5|Baltimore, MD|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Foreign Born|2012|Both|All|12.0|Philadelphia, PA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Foreign Born|2012|Both|All|14.3|San Antonio, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Foreign Born|2012|Both|All|14.3|Washington, DC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Foreign Born|2012|Both|All|14.5|Minneapolis, MN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Foreign Born|2012|Both|All|15.4|Denver, CO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Foreign Born|2012|Both|All|18.6|Seattle, WA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Foreign Born|2012|Both|All|20.5|Phoenix, AZ|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Foreign Born|2012|Both|All|21.4|Chicago, Il|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Foreign Born|2012|Both|All|24.2|Dallas, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Foreign Born|2012|Both|All|26.2|Boston, MA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Foreign Born|2012|Both|All|27.1|Oakland (Alameda County), CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Foreign Born|2012|Both|All|27.9|Houston, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Foreign Born|2012|Both|All|35.9|San Francisco, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Foreign Born|2012|Both|All|37.6|New York City, NY|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Foreign Born|2012|Both|All|38.2|San Jose, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Foreign Born|2012|Both|All|38.6|Los Angeles, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Foreign Born|2013|Both|All|4.4|Cleveland, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Foreign Born|2013|Both|All|5.0|Detroit, MI|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Foreign Born|2013|Both|All|7.1|Baltimore, MD|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Foreign Born|2013|Both|All|8.4|Kansas City, MO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Foreign Born|2013|Both|All|12.7|Philadelphia, PA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Foreign Born|2013|Both|All|12.9|U.S. Total, U.S. Total|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2013|Both|All|14.2|San Antonio, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Foreign Born|2013|Both|All|14.4|Washington, DC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Foreign Born|2013|Both|All|15.2|Denver, CO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Foreign Born|2013|Both|All|15.6|Fort Worth (Tarrant County), TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Foreign Born|2013|Both|All|16.1|Minneapolis, MN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Foreign Born|2013|Both|All|17.7|Seattle, WA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Foreign Born|2013|Both|All|19.8|Phoenix, AZ|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Foreign Born|2013|Both|All|21.1|Chicago, Il|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Foreign Born|2013|Both|All|21.8|Las Vegas (Clark County), NV|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Foreign Born|2013|Both|All|23.4|San Diego County, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Foreign Born|2013|Both|All|24.4|Dallas, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Foreign Born|2013|Both|All|25.9|Oakland (Alameda County), CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Foreign Born|2013|Both|All|27.7|Boston, MA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Foreign Born|2013|Both|All|28.3|Houston, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Foreign Born|2013|Both|All|34.9|San Francisco, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Foreign Born|2013|Both|All|37.0|New York City, NY|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Foreign Born|2013|Both|All|38.3|Los Angeles, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Foreign Born|2013|Both|All|39.3|San Jose, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Foreign Born|2013|Both|All|51.3|Miami (Miami-Dade County), FL|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent Foreign Born|2014|Both|All|5.1|Cleveland, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Foreign Born|2014|Both|All|5.7|Detroit, MI|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Foreign Born|2014|Both|All|7.1|Kansas City, MO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Foreign Born|2014|Both|All|7.8|Baltimore, MD|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Foreign Born|2014|Both|All|8.8|Indianapolis (Marion County), IN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2014|Both|All|9.9|Columbus, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2014|Both|All|13.1|Philadelphia, PA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Foreign Born|2014|Both|All|13.3|U.S. Total, U.S. Total|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2014|Both|All|14.0|Washington, DC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Foreign Born|2014|Both|All|14.1|San Antonio, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Foreign Born|2014|Both|All|14.2|Portland (Multnomah County), OR|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Foreign Born|2014|Both|All|14.3|Charlotte, NC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2014|Both|All|15.2|Minneapolis, MN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Foreign Born|2014|Both|All|15.5|Fort Worth (Tarrant County), TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Foreign Born|2014|Both|All|17.0|Denver, CO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Foreign Born|2014|Both|All|17.7|Austin, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2014|Both|All|18.1|Seattle, WA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Foreign Born|2014|Both|All|20.3|Phoenix, AZ|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Foreign Born|2014|Both|All|20.5|Chicago, Il|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Foreign Born|2014|Both|All|22.2|Las Vegas (Clark County), NV|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Foreign Born|2014|Both|All|23.2|San Diego County, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Foreign Born|2014|Both|All|23.7|Dallas, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Foreign Born|2014|Both|All|27.2|Long Beach, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Foreign Born|2014|Both|All|27.6|Boston, MA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Foreign Born|2014|Both|All|28.6|Oakland (Alameda County), CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Foreign Born|2014|Both|All|29.3|Houston, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Foreign Born|2014|Both|All|34.4|San Francisco, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Foreign Born|2014|Both|All|37.2|New York City, NY|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Foreign Born|2014|Both|All|38.1|Los Angeles, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Foreign Born|2014|Both|All|39.6|San Jose, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Foreign Born|2014|Both|All|51.6|Miami (Miami-Dade County), FL|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent Foreign Born|2015|Both|All|4.9|Cleveland, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Foreign Born|2015|Both|All|5.9|Detroit, MI|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Foreign Born|2015|Both|All|8.0|Baltimore, MD|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Foreign Born|2015|Both|All|8.0|Kansas City, MO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Foreign Born|2015|Both|All|8.5|Indianapolis (Marion County), IN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2015|Both|All|10.5|Columbus, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2015|Both|All|13.1|Philadelphia, PA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Foreign Born|2015|Both|All|13.5|U.S. Total, U.S. Total|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2015|Both|All|13.8|Portland (Multnomah County), OR|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Foreign Born|2015|Both|All|14.2|San Antonio, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Foreign Born|2015|Both|All|14.2|Washington, DC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Foreign Born|2015|Both|All|14.5|Charlotte, NC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2015|Both|All|15.9|Fort Worth (Tarrant County), TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Foreign Born|2015|Both|All|16.6|Denver, CO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Foreign Born|2015|Both|All|17.2|Minneapolis, MN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Foreign Born|2015|Both|All|17.5|Seattle, WA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Foreign Born|2015|Both|All|18.0|Austin, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2015|Both|All|19.2|Phoenix, AZ|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Foreign Born|2015|Both|All|21.1|Chicago, Il|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Foreign Born|2015|Both|All|22.2|Las Vegas (Clark County), NV|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Foreign Born|2015|Both|All|24.2|San Diego County, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Foreign Born|2015|Both|All|24.4|Dallas, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Foreign Born|2015|Both|All|26.9|Long Beach, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Foreign Born|2015|Both|All|27.2|Oakland (Alameda County), CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Foreign Born|2015|Both|All|28.4|Boston, MA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Foreign Born|2015|Both|All|30.3|Houston, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Foreign Born|2015|Both|All|34.4|San Francisco, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Foreign Born|2015|Both|All|37.4|Los Angeles, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Foreign Born|2015|Both|All|37.6|New York City, NY|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Foreign Born|2015|Both|All|39.1|San Jose, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Foreign Born|2015|Both|All|52.7|Miami (Miami-Dade County), FL|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent Foreign Born|2016|Both|All|5.5|Cleveland, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Foreign Born|2016|Both|All|5.9|Detroit, MI|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Foreign Born|2016|Both|All|8.0|Kansas City, MO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Foreign Born|2016|Both|All|8.1|Baltimore, MD|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Foreign Born|2016|Both|All|9.5|Indianapolis (Marion County), IN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2016|Both|All|10.2|Columbus, OH|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2016|Both|All|13.3|Washington, DC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Foreign Born|2016|Both|All|13.5|U.S. Total, U.S. Total|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2016|Both|All|13.6|Portland (Multnomah County), OR|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Foreign Born|2016|Both|All|14.7|San Antonio, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Foreign Born|2016|Both|All|14.8|Philadelphia, PA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Foreign Born|2016|Both|All|14.9|Denver, CO|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Foreign Born|2016|Both|All|15.2|Charlotte, NC|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2016|Both|All|15.4|Minneapolis, MN|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Foreign Born|2016|Both|All|16.3|Fort Worth (Tarrant County), TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Foreign Born|2016|Both|All|16.9|Austin, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||||||
Demographics|Percent Foreign Born|2016|Both|All|18.7|Seattle, WA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Foreign Born|2016|Both|All|19.9|Phoenix, AZ|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Foreign Born|2016|Both|All|20.7|Chicago, Il|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Foreign Born|2016|Both|All|22.8|Las Vegas (Clark County), NV|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Foreign Born|2016|Both|All|24.1|San Diego County, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Foreign Born|2016|Both|All|24.4|Dallas, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Foreign Born|2016|Both|All|26.2|Long Beach, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Foreign Born|2016|Both|All|27.9|Oakland (Alameda County), CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Foreign Born|2016|Both|All|28.9|Boston, MA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Foreign Born|2016|Both|All|30.3|Houston, TX|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Foreign Born|2016|Both|All|35.1|San Francisco, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Foreign Born|2016|Both|All|37.5|New York City, NY|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Foreign Born|2016|Both|All|37.7|Los Angeles, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Foreign Born|2016|Both|All|39.3|San Jose, CA|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Foreign Born|2016|Both|All|53.8|Miami (Miami-Dade County), FL|Percentage of the population foreign born (including naturalized US citizens and not US citizens) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimate; Table ID B05002 - Place of Birth by Nativity and Citizenship Status||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population 65 and Over|2012|Both|All|8.1|Minneapolis, MN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.5|8.7||
Demographics|Percent of Population 65 and Over|2012|Both|All|9.2|Dallas, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|9.1|9.3||
Demographics|Percent of Population 65 and Over|2012|Both|All|9.2|Phoenix, AZ|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.8|9.6||
Demographics|Percent of Population 65 and Over|2012|Both|All|9.6|Fort Worth (Tarrant County), TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|9.5|9.7||
Demographics|Percent of Population 65 and Over|2012|Both|All|9.6|Houston, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|9.3|9.9||
Demographics|Percent of Population 65 and Over|2012|Both|All|9.8|Long Beach, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.0|10.6||
Demographics|Percent of Population 65 and Over|2012|Both|All|10.5|Boston, MA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|10.3|10.7||
Demographics|Percent of Population 65 and Over|2012|Both|All|10.5|Denver, CO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|10.4|10.6||
Demographics|Percent of Population 65 and Over|2012|Both|All|10.9|Chicago, Il|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.6|11.2||
Demographics|Percent of Population 65 and Over|2012|Both|All|10.9|San Jose, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|10.5|11.3||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.0|Los Angeles, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.8|11.2||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.2|San Antonio, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|11.0|11.4||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.3|Oakland (Alameda County), CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|10.6|12.0||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.4|Seattle, WA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|10.8|12.0||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.4|Washington, DC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|11.3|11.5||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.6|Kansas City, MO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|11.1|12.1||
Demographics|Percent of Population 65 and Over|2012|Both|All|11.9|Baltimore, MD|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|11.8|12.0||
Demographics|Percent of Population 65 and Over|2012|Both|All|12.0|San Diego County, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|11.9|12.1||
Demographics|Percent of Population 65 and Over|2012|Both|All|12.2|Cleveland, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|11.6|12.8||
Demographics|Percent of Population 65 and Over|2012|Both|All|12.2|Detroit, MI|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|11.9|12.5||
Demographics|Percent of Population 65 and Over|2012|Both|All|12.2|Philadelphia, PA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.1|12.3||
Demographics|Percent of Population 65 and Over|2012|Both|All|12.3|Las Vegas (Clark County), NV|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|12.2|12.4||
Demographics|Percent of Population 65 and Over|2012|Both|All|12.5|New York City, NY|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|12.4|12.6||
Demographics|Percent of Population 65 and Over|2012|Both|All|13.7|U.S. Total, U.S. Total|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||13.6|13.8||
Demographics|Percent of Population 65 and Over|2012|Both|All|14.0|San Francisco, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|13.9|14.1||
Demographics|Percent of Population 65 and Over|2012|Both|All|14.5|Miami (Miami-Dade County), FL|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|14.4|14.6||
Demographics|Percent of Population 65 and Over|2013|Both|All|8.8|Minneapolis, MN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.2|9.4||
Demographics|Percent of Population 65 and Over|2013|Both|All|9.1|Phoenix, AZ|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.8|9.4||
Demographics|Percent of Population 65 and Over|2013|Both|All|9.5|Dallas, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|9.4|9.6||
Demographics|Percent of Population 65 and Over|2013|Both|All|9.5|Houston, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|9.3|9.7||
Demographics|Percent of Population 65 and Over|2013|Both|All|9.9|Fort Worth (Tarrant County), TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|9.8|10.0||
Demographics|Percent of Population 65 and Over|2013|Both|All|10.4|Long Beach, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.6|11.2||
Demographics|Percent of Population 65 and Over|2013|Both|All|10.6|Boston, MA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|10.3|10.9||
Demographics|Percent of Population 65 and Over|2013|Both|All|11.0|Los Angeles, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.8|11.2||
Demographics|Percent of Population 65 and Over|2013|Both|All|11.2|San Jose, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|10.8|11.6||
Demographics|Percent of Population 65 and Over|2013|Both|All|11.4|Seattle, WA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|10.7|12.1||
Demographics|Percent of Population 65 and Over|2013|Both|All|11.4|Washington, DC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|11.3|11.5||
Demographics|Percent of Population 65 and Over|2013|Both|All|11.7|Oakland (Alameda County), CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|11.0|12.4||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.0|Baltimore, MD|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|11.9|12.1||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.0|Kansas City, MO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|11.5|12.5||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.3|San Diego County, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|12.2|12.4||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.4|Cleveland, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|11.9|12.9||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.4|Philadelphia, PA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.3|12.5||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.6|Detroit, MI|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|12.3|12.9||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.8|Las Vegas (Clark County), NV|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population 65 and Over|2013|Both|All|12.8|New York City, NY|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|12.7|12.9||
Demographics|Percent of Population 65 and Over|2013|Both|All|14.1|U.S. Total, U.S. Total|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||14.0|14.2||
Demographics|Percent of Population 65 and Over|2013|Both|All|14.2|San Francisco, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|14.1|14.3||
Demographics|Percent of Population 65 and Over|2013|Both|All|14.9|Miami (Miami-Dade County), FL|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|14.8|15.0||
Demographics|Percent of Population 65 and Over|2014|Both|All|8.3|Minneapolis, MN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.7|8.9||
Demographics|Percent of Population 65 and Over|2014|Both|All|8.4|Austin, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2014|Both|All|9.7|Dallas, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|9.6|9.8||
Demographics|Percent of Population 65 and Over|2014|Both|All|9.8|Houston, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|9.5|10.1||
Demographics|Percent of Population 65 and Over|2014|Both|All|9.8|Phoenix, AZ|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|9.5|10.1||
Demographics|Percent of Population 65 and Over|2014|Both|All|10.0|Charlotte, NC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2014|Both|All|10.0|Long Beach, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.2|10.8||
Demographics|Percent of Population 65 and Over|2014|Both|All|10.2|Fort Worth (Tarrant County), TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|10.1|10.3||
Demographics|Percent of Population 65 and Over|2014|Both|All|10.3|Boston, MA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|10.0|10.6||
Demographics|Percent of Population 65 and Over|2014|Both|All|10.9|Columbus, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2014|Both|All|10.9|Denver, CO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|10.8|11.0||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.3|Indianapolis (Marion County), IN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.3|Washington, DC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|11.2|11.4||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.4|Chicago, Il|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.1|11.7||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.6|Los Angeles, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|11.4|11.8||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.6|San Antonio, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|11.4|11.8||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.7|San Jose, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|11.4|12.0||
Demographics|Percent of Population 65 and Over|2014|Both|All|11.9|Oakland (Alameda County), CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|11.3|12.5||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.0|Kansas City, MO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|11.5|12.5||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.2|Cleveland, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|11.6|12.8||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.2|Seattle, WA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|11.7|12.7||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.3|Baltimore, MD|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|12.2|12.4||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.5|Detroit, MI|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|12.1|12.9||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.5|Philadelphia, PA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.4|12.6||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.7|San Diego County, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|12.6|12.8||
Demographics|Percent of Population 65 and Over|2014|Both|All|12.9|New York City, NY|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|12.8|13.0||
Demographics|Percent of Population 65 and Over|2014|Both|All|13.3|Las Vegas (Clark County), NV|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|13.2|13.4||
Demographics|Percent of Population 65 and Over|2014|Both|All|14.4|San Francisco, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|14.3|14.5||
Demographics|Percent of Population 65 and Over|2014|Both|All|14.5|U.S. Total, U.S. Total|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||14.4|14.6||
Demographics|Percent of Population 65 and Over|2014|Both|All|15.2|Miami (Miami-Dade County), FL|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|15.1|15.3||
Demographics|Percent of Population 65 and Over|2015|Both|All|8.7|Austin, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2015|Both|All|9.2|Minneapolis, MN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|9.6|Houston, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|9.9|Dallas, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|10.4|Phoenix, AZ|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|10.5|Fort Worth (Tarrant County), TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|10.6|Boston, MA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.1|Columbus, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.1|Denver, CO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.2|Long Beach, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.3|Seattle, WA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.4|Washington, DC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.5|Indianapolis (Marion County), IN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.5|Los Angeles, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.6|Chicago, Il|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.6|San Antonio, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.7|Oakland (Alameda County), CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|11.8|San Jose, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|12.2|Portland (Multnomah County), OR|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|12.3|Kansas City, MO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|12.5|Baltimore, MD|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|12.7|Detroit, MI|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|12.7|Philadelphia, PA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|13.1|San Diego County, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|13.2|New York City, NY|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|13.5|Cleveland, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|13.7|Las Vegas (Clark County), NV|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|14.6|San Francisco, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population 65 and Over|2015|Both|All|14.9|U.S. Total, U.S. Total|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2015|Both|All|15.6|Miami (Miami-Dade County), FL|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|9.0|Austin, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2016|Both|All|9.3|Minneapolis, MN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|10.1|Dallas, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|10.2|Houston, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|10.4|Phoenix, AZ|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|10.5|Long Beach, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|10.6|Charlotte, NC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2016|Both|All|10.8|Fort Worth (Tarrant County), TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.0|Boston, MA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.2|Denver, CO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.3|Columbus, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.5|San Jose, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.6|Washington, DC|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.8|Chicago, Il|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.8|Indianapolis (Marion County), IN|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2016|Both|All|11.9|San Antonio, TX|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.1|Los Angeles, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.3|Seattle, WA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.4|Kansas City, MO|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.6|Portland (Multnomah County), OR|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.7|Detroit, MI|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.7|Oakland (Alameda County), CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.9|Baltimore, MD|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|12.9|Philadelphia, PA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|13.1|Cleveland, OH|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|13.4|San Diego County, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|13.5|New York City, NY|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|14.1|Las Vegas (Clark County), NV|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|14.9|San Francisco, CA|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population 65 and Over|2016|Both|All|15.2|U.S. Total, U.S. Total|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Percent of Population 65 and Over|2016|Both|All|16.0|Miami (Miami-Dade County), FL|Percent of population 65 years and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population Under 18|2012|Both|All|13.4|San Francisco, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population Under 18|2012|Both|All|15.3|Seattle, WA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population Under 18|2012|Both|All|17.3|Boston, MA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population Under 18|2012|Both|All|17.3|Washington, DC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population Under 18|2012|Both|All|20.7|Minneapolis, MN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population Under 18|2012|Both|All|21.1|Miami (Miami-Dade County), FL|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population Under 18|2012|Both|All|21.4|New York City, NY|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population Under 18|2012|Both|All|21.5|Baltimore, MD|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population Under 18|2012|Both|All|21.7|Denver, CO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent of Population Under 18|2012|Both|All|22.5|Los Angeles, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population Under 18|2012|Both|All|22.5|Philadelphia, PA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population Under 18|2012|Both|All|22.6|Chicago, Il|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population Under 18|2012|Both|All|22.9|San Diego County, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population Under 18|2012|Both|All|23.5|Cleveland, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population Under 18|2012|Both|All|23.5|U.S. Total, U.S. Total|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2012|Both|All|24.3|San Jose, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population Under 18|2012|Both|All|24.4|Kansas City, MO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population Under 18|2012|Both|All|24.5|Las Vegas (Clark County), NV|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population Under 18|2012|Both|All|24.6|Long Beach, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population Under 18|2012|Both|All|25.4|Detroit, MI|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population Under 18|2012|Both|All|25.6|Houston, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population Under 18|2012|Both|All|26.1|San Antonio, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent of Population Under 18|2012|Both|All|27.1|Phoenix, AZ|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population Under 18|2012|Both|All|27.3|Dallas, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population Under 18|2012|Both|All|27.5|Fort Worth (Tarrant County), TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population Under 18|2013|Both|All|13.4|San Francisco, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population Under 18|2013|Both|All|15.4|Seattle, WA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population Under 18|2013|Both|All|16.6|Boston, MA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population Under 18|2013|Both|All|17.3|Washington, DC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population Under 18|2013|Both|All|19.6|Minneapolis, MN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population Under 18|2013|Both|All|20.5|Oakland (Alameda County), CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent of Population Under 18|2013|Both|All|20.8|Miami (Miami-Dade County), FL|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population Under 18|2013|Both|All|21.0|Denver, CO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent of Population Under 18|2013|Both|All|21.1|Baltimore, MD|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population Under 18|2013|Both|All|21.2|New York City, NY|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population Under 18|2013|Both|All|22.0|Chicago, Il|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population Under 18|2013|Both|All|22.0|Los Angeles, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population Under 18|2013|Both|All|22.2|Philadelphia, PA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population Under 18|2013|Both|All|22.6|San Diego County, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population Under 18|2013|Both|All|23.0|Kansas City, MO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population Under 18|2013|Both|All|23.3|Cleveland, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population Under 18|2013|Both|All|23.3|U.S. Total, U.S. Total|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2013|Both|All|24.0|San Jose, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population Under 18|2013|Both|All|24.1|Las Vegas (Clark County), NV|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population Under 18|2013|Both|All|24.6|Long Beach, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population Under 18|2013|Both|All|24.9|Detroit, MI|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population Under 18|2013|Both|All|25.1|Houston, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population Under 18|2013|Both|All|25.6|San Antonio, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent of Population Under 18|2013|Both|All|26.7|Phoenix, AZ|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population Under 18|2013|Both|All|27.1|Dallas, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population Under 18|2013|Both|All|27.3|Fort Worth (Tarrant County), TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population Under 18|2014|Both|All|13.4|San Francisco, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population Under 18|2014|Both|All|14.9|Seattle, WA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population Under 18|2014|Both|All|16.5|Boston, MA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population Under 18|2014|Both|All|16.6|Boston, MA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population Under 18|2014|Both|All|17.5|Washington, DC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population Under 18|2014|Both|All|19.8|Portland (Multnomah County), OR|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent of Population Under 18|2014|Both|All|20.3|Minneapolis, MN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population Under 18|2014|Both|All|20.6|Miami (Miami-Dade County), FL|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population Under 18|2014|Both|All|21.1|Oakland (Alameda County), CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent of Population Under 18|2014|Both|All|21.2|Baltimore, MD|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population Under 18|2014|Both|All|21.2|New York City, NY|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population Under 18|2014|Both|All|21.8|Los Angeles, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population Under 18|2014|Both|All|22.0|Chicago, Il|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population Under 18|2014|Both|All|22.2|Philadelphia, PA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population Under 18|2014|Both|All|22.3|San Diego County, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population Under 18|2014|Both|All|23.0|Austin, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2014|Both|All|23.1|San Jose, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population Under 18|2014|Both|All|23.1|U.S. Total, U.S. Total|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2014|Both|All|23.5|Kansas City, MO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population Under 18|2014|Both|All|23.7|Columbus, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2014|Both|All|23.8|Las Vegas (Clark County), NV|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population Under 18|2014|Both|All|24.2|Cleveland, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population Under 18|2014|Both|All|24.4|Long Beach, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population Under 18|2014|Both|All|24.6|Charlotte, NC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2014|Both|All|24.8|Detroit, MI|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population Under 18|2014|Both|All|24.9|Indianapolis (Marion County), IN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2014|Both|All|25.0|Houston, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population Under 18|2014|Both|All|25.7|San Antonio, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent of Population Under 18|2014|Both|All|26.9|Dallas, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population Under 18|2014|Both|All|26.9|Phoenix, AZ|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population Under 18|2014|Both|All|27.1|Fort Worth (Tarrant County), TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population Under 18|2015|Both|All|13.4|San Francisco, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population Under 18|2015|Both|All|14.6|Seattle, WA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population Under 18|2015|Both|All|16.7|Boston, MA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population Under 18|2015|Both|All|17.5|Washington, DC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population Under 18|2015|Both|All|19.6|Portland (Multnomah County), OR|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent of Population Under 18|2015|Both|All|19.9|Minneapolis, MN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population Under 18|2015|Both|All|20.3|Oakland (Alameda County), CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent of Population Under 18|2015|Both|All|20.5|Miami (Miami-Dade County), FL|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population Under 18|2015|Both|All|20.6|Denver, CO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent of Population Under 18|2015|Both|All|21.1|Baltimore, MD|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population Under 18|2015|Both|All|21.1|New York City, NY|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population Under 18|2015|Both|All|21.4|Chicago, Il|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population Under 18|2015|Both|All|21.5|Los Angeles, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population Under 18|2015|Both|All|22.1|Philadelphia, PA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population Under 18|2015|Both|All|22.1|San Diego County, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population Under 18|2015|Both|All|22.5|Cleveland, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population Under 18|2015|Both|All|22.8|Austin, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2015|Both|All|22.9|U.S. Total, U.S. Total|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2015|Both|All|23.0|San Jose, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population Under 18|2015|Both|All|23.3|Kansas City, MO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population Under 18|2015|Both|All|23.4|Long Beach, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population Under 18|2015|Both|All|23.6|Columbus, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2015|Both|All|23.6|Las Vegas (Clark County), NV|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population Under 18|2015|Both|All|24.4|Charlotte, NC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2015|Both|All|25.0|Indianapolis (Marion County), IN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2015|Both|All|25.3|Detroit, MI|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population Under 18|2015|Both|All|25.6|Houston, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population Under 18|2015|Both|All|26.3|Phoenix, AZ|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population Under 18|2015|Both|All|26.7|Dallas, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population Under 18|2015|Both|All|26.9|Fort Worth (Tarrant County), TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population Under 18|2016|Both|All|13.6|San Francisco, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent of Population Under 18|2016|Both|All|15.1|Seattle, WA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent of Population Under 18|2016|Both|All|16.1|Boston, MA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent of Population Under 18|2016|Both|All|17.7|Washington, DC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent of Population Under 18|2016|Both|All|19.3|Portland (Multnomah County), OR|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent of Population Under 18|2016|Both|All|19.7|Minneapolis, MN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent of Population Under 18|2016|Both|All|19.7|Oakland (Alameda County), CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent of Population Under 18|2016|Both|All|20.3|Denver, CO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent of Population Under 18|2016|Both|All|20.4|Miami (Miami-Dade County), FL|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent of Population Under 18|2016|Both|All|20.8|Los Angeles, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent of Population Under 18|2016|Both|All|21.0|Baltimore, MD|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent of Population Under 18|2016|Both|All|21.1|New York City, NY|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent of Population Under 18|2016|Both|All|21.2|Chicago, Il|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent of Population Under 18|2016|Both|All|22.0|San Diego County, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent of Population Under 18|2016|Both|All|22.1|Philadelphia, PA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent of Population Under 18|2016|Both|All|22.3|Cleveland, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent of Population Under 18|2016|Both|All|22.5|Austin, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2016|Both|All|22.8|U.S. Total, U.S. Total|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2016|Both|All|23.1|San Jose, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent of Population Under 18|2016|Both|All|23.3|Kansas City, MO|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent of Population Under 18|2016|Both|All|23.4|Long Beach, CA|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent of Population Under 18|2016|Both|All|23.5|Columbus, OH|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2016|Both|All|23.5|Las Vegas (Clark County), NV|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent of Population Under 18|2016|Both|All|24.2|Charlotte, NC|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2016|Both|All|24.9|Indianapolis (Marion County), IN|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||||||
Demographics|Percent of Population Under 18|2016|Both|All|25.3|Houston, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent of Population Under 18|2016|Both|All|25.3|San Antonio, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent of Population Under 18|2016|Both|All|25.7|Detroit, MI|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent of Population Under 18|2016|Both|All|26.7|Dallas, TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent of Population Under 18|2016|Both|All|26.7|Fort Worth (Tarrant County), TX|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent of Population Under 18|2016|Both|All|27.0|Phoenix, AZ|Percent of population under 18 years of age calculated using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table IDs B09001 - Population Under 18 Years by Age & DP05 - Demographic and Housing Estimates. Population under 18 years of age (B09001) divided by total population (DP05).||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent of Population with a Disability|2012|Both|All|8.2|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.0|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.2|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.3|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.3|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.7|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.8|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|9.8|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.0|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.2|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.3|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.4|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.6|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|10.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|11.3|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|11.5|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|11.5|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|11.7|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|12.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|12.2|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|12.6|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|13.6|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|15.1|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|16.0|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|19.0|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|All|19.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|10.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|12.8|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|13.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|13.4|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|13.8|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|14.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|14.6|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|15.0|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|15.0|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|15.1|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|15.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|15.2|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|15.8|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|16.0|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|16.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|16.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|16.7|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|17.5|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|17.8|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|18.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|18.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|21.9|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|23.9|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|24.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|24.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|25.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|28.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|28.3|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|American Indian/Alaska Native|39.2|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|4.0|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|4.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|4.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|4.3|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|5.1|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|5.5|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|5.9|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.0|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.2|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.4|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.5|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|6.8|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|7.0|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|7.4|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.1|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.3|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.8|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.8|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|8.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|10.2|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Asian/PI|11.4|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2012|Both|Black|10.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|11.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|11.4|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|11.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|11.8|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|11.8|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|12.6|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|12.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|12.8|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|13.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|13.6|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|13.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|13.7|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|14.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|14.8|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|15.1|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|15.2|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|15.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|15.4|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|15.6|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|15.9|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|16.1|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|16.7|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|17.0|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|17.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|17.9|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|19.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|19.8|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Black|22.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|5.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|5.9|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.0|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.3|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.4|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.5|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.6|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.8|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|6.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|7.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|7.1|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|7.1|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|7.2|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|7.3|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|7.6|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|8.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|8.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|8.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|9.6|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|9.7|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|10.4|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|11.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|12.9|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|13.2|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|16.9|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Hispanic|18.0|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|6.6|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|7.2|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|7.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.0|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.3|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.3|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.5|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.8|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|8.8|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|9.0|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|9.8|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.2|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.4|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.4|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|10.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|11.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|11.3|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|12.3|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|13.9|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|14.1|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|14.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|14.6|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|16.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Multiracial|20.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|Other|5.0|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|5.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|5.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|5.5|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|5.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|6.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|6.4|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|6.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|6.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|6.7|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.0|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.1|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.1|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.2|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.6|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.9|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|7.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|8.5|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|9.8|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|10.0|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|10.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|10.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|12.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|14.3|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|14.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|Other|18.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2012|Both|White|5.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|8.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|9.0|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|9.4|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|9.5|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|9.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|9.9|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|10.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|10.9|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.1|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.2|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.6|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.7|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|11.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|12.0|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|12.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|12.9|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|13.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|13.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|13.5|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|14.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|14.9|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|19.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Both|White|22.9|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|8.6|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|9.3|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|9.5|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|9.7|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|9.8|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.3|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.4|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.4|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.5|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.5|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|10.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|11.3|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|11.5|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|11.5|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|11.5|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|11.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|11.9|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|12.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|12.2|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|12.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|12.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|13.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|14.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|15.8|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|17.0|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|19.9|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Female|All|19.9|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|7.7|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|8.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|8.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|8.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|8.8|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|8.9|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.0|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.2|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.3|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.6|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.8|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|9.9|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|10.0|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|10.1|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|10.7|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|10.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|11.0|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|11.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|11.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|11.8|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|11.9|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|13.2|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|14.3|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|14.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|18.1|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2012|Male|All|18.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|8.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|8.9|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.4|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.4|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.6|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|9.9|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|10.0|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|10.1|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|10.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|10.3|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|10.6|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|10.9|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|11.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|11.7|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|11.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|12.1|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|12.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|12.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|12.9|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|13.6|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|15.3|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|15.8|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|19.1|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|All|19.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|9.8|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|10.0|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|12.0|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|12.4|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|13.6|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|13.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|14.8|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|14.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|14.9|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|15.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|15.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|16.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|16.2|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|16.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|17.6|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|18.0|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|18.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|18.6|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|19.1|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|19.1|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|19.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|23.0|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|23.4|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|23.6|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|26.2|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|26.9|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|28.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|29.1|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|American Indian/Alaska Native|37.7|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|3.7|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|4.2|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|4.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|5.0|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|5.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|5.7|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.5|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.7|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.8|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|6.9|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|7.4|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|7.5|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|7.6|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|7.9|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.2|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.2|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.7|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|8.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|10.4|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Asian/PI|11.6|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2013|Both|Black|10.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|11.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|11.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|11.7|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|12.0|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|12.1|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|12.5|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|12.9|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|13.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|13.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|13.8|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|13.9|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|14.4|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|14.7|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|14.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|15.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|15.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|15.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|15.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|16.0|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|16.0|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|16.8|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|17.0|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|17.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|17.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|19.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|20.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Black|21.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|5.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|5.8|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.2|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.3|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.4|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.4|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.8|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|6.8|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|7.0|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|7.1|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|7.2|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|7.3|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|7.3|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|7.5|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|8.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|8.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|8.4|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|8.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|9.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|9.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|10.1|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|11.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|12.9|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|13.5|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|16.8|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Hispanic|18.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|6.4|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|7.2|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|7.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|7.7|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|8.1|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|8.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|8.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|8.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|8.9|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|8.9|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|9.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|9.2|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.0|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.1|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.3|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.8|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.8|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|10.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|11.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|11.6|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|11.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|12.6|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|12.7|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|13.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|14.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|15.8|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|16.6|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Multiracial|19.9|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|Other|5.0|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|5.2|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|5.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|5.6|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|5.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|5.7|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|6.1|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|6.2|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|6.3|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|6.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|6.6|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|6.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|7.0|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|7.1|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|7.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|7.6|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|7.7|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|8.0|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|8.0|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|8.0|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|8.1|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|9.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|10.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|12.6|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|12.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|14.9|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|17.1|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|Other|18.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2013|Both|White|5.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|8.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|9.0|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|9.4|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|9.5|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|9.6|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|9.7|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|10.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|10.8|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|10.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|10.9|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|11.2|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|11.3|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|11.5|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|11.6|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|11.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|11.9|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|12.2|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|12.2|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|12.9|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|13.2|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|13.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|13.7|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|13.9|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|14.5|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|19.8|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Both|White|23.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|8.5|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|9.2|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|9.6|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|9.8|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|9.8|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.0|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.0|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.4|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.4|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.4|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|10.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|11.2|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|11.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|11.4|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|11.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|11.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|12.2|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|12.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|12.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|12.4|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|12.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|13.5|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|13.8|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|15.8|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|16.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|19.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Female|All|20.0|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|7.7|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|8.4|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|8.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|8.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|8.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.2|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.4|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.8|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|9.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|10.0|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|10.5|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|10.7|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|11.0|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|11.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|11.9|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|12.1|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|12.1|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|12.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|13.4|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|14.6|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|14.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|18.7|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2013|Male|All|18.9|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|8.2|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.1|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.2|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.5|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.5|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.7|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.8|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|9.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|10.0|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|10.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|10.3|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|10.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|10.5|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|10.8|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|11.1|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|11.5|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|11.9|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|11.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|12.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|12.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|12.6|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|13.4|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|13.8|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|15.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|15.6|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|19.0|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|All|19.6|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|10.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|10.9|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|12.2|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|12.8|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|13.4|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|13.5|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|14.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|14.3|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|14.5|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|16.3|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|16.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|16.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|16.7|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|17.1|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|17.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|18.5|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|19.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|19.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|20.4|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|20.8|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|22.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|23.1|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|23.2|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|24.6|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|27.9|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|28.5|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|29.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|32.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|American Indian/Alaska Native|44.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|4.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|4.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|5.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|5.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|5.3|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|5.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.1|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.2|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.5|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.5|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.6|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.8|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|6.8|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|7.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|7.3|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|7.4|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|7.5|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.0|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.0|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.3|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.4|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.5|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.6|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|8.8|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|9.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|10.0|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Asian/PI|10.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2014|Both|Black|10.8|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|11.2|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|11.7|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|11.8|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|11.9|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|12.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|12.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|12.9|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|13.3|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|13.7|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|13.8|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|14.3|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|14.8|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|14.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|14.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|15.7|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|15.7|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|15.9|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|15.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|16.2|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|16.4|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|16.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|16.8|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|17.0|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|17.2|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|17.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|19.6|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|20.4|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Black|21.6|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|5.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|5.6|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.3|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.3|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.4|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.4|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.6|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.6|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|6.8|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.1|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.2|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.7|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|7.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|8.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|8.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|8.5|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|8.8|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|9.3|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|10.1|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|10.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|11.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|13.2|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|13.6|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|17.0|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Hispanic|18.5|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|6.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|7.2|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|7.6|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|8.1|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|8.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|8.5|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|8.8|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|8.9|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|9.1|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|9.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|9.3|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|9.3|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|10.0|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|10.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|10.4|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|10.4|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|11.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|11.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|11.4|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|11.6|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|11.6|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|11.6|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|12.2|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|12.5|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|13.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|14.3|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|15.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|18.0|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Multiracial|21.7|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.3|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.4|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|5.9|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|6.1|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|6.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|6.3|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|6.6|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|6.7|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.2|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.3|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.6|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.8|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.8|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|7.9|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|8.1|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|8.6|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|9.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|9.8|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|9.8|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|11.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|13.3|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|14.6|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|16.6|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|Other|18.6|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2014|Both|White|5.1|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|8.8|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|9.4|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|9.4|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|9.4|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|9.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|9.8|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|10.3|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|10.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.4|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.4|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.4|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.5|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|11.7|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|12.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|12.3|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|12.3|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|12.6|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|13.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|13.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|13.4|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|14.4|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|14.5|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|14.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|15.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|19.2|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Both|White|22.3|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|8.7|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|9.5|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|9.6|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|9.9|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|9.9|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.1|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.5|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.8|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|10.9|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|11.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|11.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|11.6|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|11.7|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|11.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|12.4|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|12.4|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|12.4|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|12.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|12.6|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|14.0|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|14.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|15.9|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|16.5|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|19.4|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Female|All|20.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|7.8|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|8.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|8.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|8.8|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.2|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.3|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.3|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.4|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|9.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|10.0|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|10.3|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|10.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|11.3|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|11.3|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|11.4|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|12.1|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|12.4|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|12.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|12.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|13.6|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|14.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|14.8|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|18.6|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2014|Male|All|19.0|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|8.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.4|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.5|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.7|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.7|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.8|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.8|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|9.8|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.0|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.2|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.4|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.5|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.8|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|10.8|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|11.2|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|11.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|12.0|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|12.1|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|12.4|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|12.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|12.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|13.8|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|14.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|15.3|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|15.9|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|19.7|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|All|19.8|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|10.9|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|11.2|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|11.6|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|11.9|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|12.9|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|13.6|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|13.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|14.0|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|15.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|16.4|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|16.5|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|16.6|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|17.3|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|17.5|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|18.0|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|18.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|19.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|20.9|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|22.3|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|22.5|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|23.0|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|23.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|25.1|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|25.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|25.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|27.7|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|30.1|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|35.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|American Indian/Alaska Native|41.4|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|4.9|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|5.1|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|5.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|5.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|5.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.3|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.7|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|6.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.1|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.2|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.4|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.5|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.8|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.9|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|7.9|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|8.4|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|8.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|8.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|8.8|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|9.0|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|9.8|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|10.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|10.4|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Asian/PI|10.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2015|Both|Black|10.9|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|11.3|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|11.8|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|11.9|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|12.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|12.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|13.0|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|13.2|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|13.5|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|13.9|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|14.1|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|14.6|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|14.9|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|15.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|15.8|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|15.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|16.1|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|16.2|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|16.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|16.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|17.1|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|17.1|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|17.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|17.2|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|17.3|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|17.5|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|20.3|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|20.8|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Black|21.2|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|5.3|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|5.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.4|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.6|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.7|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.7|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.8|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|6.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|7.0|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|7.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|7.5|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|7.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|7.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.0|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.2|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.3|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|8.7|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|9.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|10.1|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|10.2|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|11.4|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|13.5|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|13.9|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|17.0|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Hispanic|18.6|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|7.0|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|7.0|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|7.3|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|7.6|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|8.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|8.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|8.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|8.8|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|9.3|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|9.3|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|9.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|9.5|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|9.7|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|10.1|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|10.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|10.5|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|10.6|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|10.7|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|11.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|11.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|11.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|12.1|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|12.6|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|12.8|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|13.2|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|13.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|15.1|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|20.2|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Multiracial|21.6|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|Other|5.3|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|5.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|5.4|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|5.6|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|5.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|6.0|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|6.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|6.4|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|6.4|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|6.5|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|6.8|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|7.1|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|7.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|7.1|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|7.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|8.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|8.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|8.1|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|8.6|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|8.7|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|9.2|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|9.8|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|10.0|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|10.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|11.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|14.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|14.7|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|17.3|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|Other|19.0|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2015|Both|White|5.0|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|8.9|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|9.4|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|9.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|9.5|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|9.7|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|10.1|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|10.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.2|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.4|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|11.5|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|12.0|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|12.2|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|12.3|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|12.5|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|12.8|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|13.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|13.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|13.6|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|14.9|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|14.9|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|15.0|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|15.2|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|19.9|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Both|White|21.3|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|8.8|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|9.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|9.9|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.0|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.7|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|10.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|11.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|11.5|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|11.5|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|11.9|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|12.2|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|12.3|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|12.6|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|12.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|13.0|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|14.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|14.3|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|15.7|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|16.7|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|20.1|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Female|All|20.4|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|7.8|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|8.9|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.0|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.0|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.1|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.2|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.3|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.6|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.6|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|9.7|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|10.1|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|10.3|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|10.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|10.7|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|11.3|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|11.4|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|11.8|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|12.2|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|12.7|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|12.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|13.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|14.0|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|15.0|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|15.0|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|19.2|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2015|Male|All|19.2|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|8.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|9.1|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|9.2|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|9.3|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|9.5|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|9.6|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|9.6|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|10.1|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|10.1|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|10.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|10.6|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|10.7|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|11.0|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|11.0|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|11.1|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|11.3|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|11.7|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|11.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|12.3|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|12.7|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|12.8|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|13.0|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|13.2|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|13.4|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|14.9|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|14.9|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|15.8|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|19.4|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|All|20.5|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|6.6|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|10.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|10.4|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|10.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|12.9|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|13.1|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|13.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|13.6|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|16.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|17.0|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|17.0|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|17.2|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|19.7|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|20.1|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|23.2|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|26.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|29.3|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|34.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|35.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|American Indian/Alaska Native|47.2|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|2.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|3.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|4.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|5.1|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|5.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|6.6|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|6.6|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|6.7|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|6.8|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|6.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|6.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.0|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.0|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.1|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.2|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|7.5|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|8.6|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|8.6|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|9.0|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|9.1|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|9.3|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|9.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|9.7|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|9.9|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|10.9|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|11.0|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Asian/PI|11.6|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Asian alone|||||
Demographics|Percent of Population with a Disability|2016|Both|Black|10.2|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|10.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|11.0|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|11.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|11.8|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|12.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|12.9|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|13.7|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|14.1|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|14.2|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|14.3|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|14.7|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|14.7|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|14.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|15.5|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|15.8|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|15.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|16.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|16.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|16.8|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|16.8|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|16.9|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|17.0|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|17.3|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|17.6|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|17.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|20.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|22.1|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Black|24.8|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|4.9|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|5.7|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|6.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|6.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|6.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|6.6|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|6.7|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|6.8|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|7.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|7.5|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|7.7|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|7.9|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|8.0|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|8.1|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|8.3|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|8.4|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|8.9|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|9.1|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|9.7|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|9.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|10.3|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|10.5|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|10.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|11.3|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|12.5|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|12.5|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|14.8|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|15.8|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Hispanic|21.4|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|6.3|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|7.2|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|7.5|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|7.7|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|8.1|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|8.7|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|8.8|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.1|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.2|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.4|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.7|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.7|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.8|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|9.9|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|10.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|10.3|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|10.6|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|10.6|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|11.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|12.4|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|12.4|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|12.5|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|13.0|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|13.2|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|13.9|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|17.0|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|17.5|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|17.8|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Multiracial|22.1|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|Other|3.9|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|4.7|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|5.3|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|5.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|5.5|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|5.8|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|6.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|6.5|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|6.7|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|7.1|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|7.1|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|7.5|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|7.5|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|8.0|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|8.0|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|8.3|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|8.3|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|8.3|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|8.4|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|9.0|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|9.3|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|9.8|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|9.8|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|10.6|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|11.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|13.1|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|16.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|17.2|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|Other|24.8|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics|Includes Native Hawaiian and other PI|||||
Demographics|Percent of Population with a Disability|2016|Both|White|5.3|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|8.6|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|9.3|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|9.7|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|10.0|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|10.0|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|10.3|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|10.4|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|10.7|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|10.9|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|11.1|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|11.5|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|11.9|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|12.0|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|12.2|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|12.6|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|12.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|13.0|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|13.2|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|13.2|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|13.8|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|14.1|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|14.1|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|14.9|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|15.8|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|16.1|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|18.1|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Both|White|19.0|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|8.7|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|9.1|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|9.8|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|9.9|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|9.9|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|10.0|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|10.2|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|10.2|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|10.9|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|10.9|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|11.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|11.2|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|11.3|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.1|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.1|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.1|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.3|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.4|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.9|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|12.9|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|13.5|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|13.5|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|13.6|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|13.6|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|15.1|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|15.5|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|16.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|19.3|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Female|All|21.9|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|7.9|San Jose, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|8.3|Seattle, WA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|8.5|Dallas, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.0|Oakland (Alameda County), CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.1|Houston, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.2|Chicago, Il|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.2|Miami (Miami-Dade County), FL|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.4|Denver, CO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.5|Long Beach, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.7|New York City, NY|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|9.8|San Francisco, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|10.0|Los Angeles, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|10.0|San Diego County, CA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|10.1|Washington, DC|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|10.5|Fort Worth (Tarrant County), TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|10.6|Boston, MA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|11.0|Phoenix, AZ|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|11.3|Minneapolis, MN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|11.8|Columbus, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|12.4|Las Vegas (Clark County), NV|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|12.4|Portland (Multnomah County), OR|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|12.7|U.S. Total, U.S. Total|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|12.8|Kansas City, MO|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|13.2|Indianapolis (Marion County), IN|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|14.3|Baltimore, MD|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|14.7|San Antonio, TX|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|15.3|Philadelphia, PA|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|19.2|Cleveland, OH|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent of Population with a Disability|2016|Male|All|19.5|Detroit, MI|Percentage of the population with a disability (including hearing, vision, cognitive, ambulatory, self-care, or independent living difficulties), using US Census, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1810 - Disability Characteristics||||||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|28.3|Miami (Miami-Dade County), FL|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|27.7|28.9||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|39.5|Los Angeles, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|39.0|40.0||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|43.4|San Jose, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0668000 was used to isolate data for San Jose, CA.|42.2|44.6||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|50.8|New York City, NY|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3651000 was used to isolate data for New York City, NY.|50.5|51.1||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|52.8|Houston, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4835000 was used to isolate data for Houston, TX.|51.8|53.8||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|54.9|San Francisco, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0667000 was used to isolate data for San Francisco, CA.|53.8|56.0||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|55.0|San Antonio, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4865000 was used to isolate data for San Antonio, TX.|54.1|55.9||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|55.9|Long Beach, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0643000 was used to isolate data for Long Beach, CA.|53.8|58.0||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|58.6|Dallas, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48113 was used to isolate data for Dallas county, TX.|58.1|59.1||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|59.1|Oakland (Alameda County), CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0653000 was used to isolate data for Oakland, CA.|57.3|60.9||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|62.0|Phoenix, AZ|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|61.0|63.0||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|62.2|San Diego County, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 06073 was used to isolate data for San Diego County, CA.|61.6|62.8||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|63.2|Chicago, Il|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|62.5|63.9||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|63.4|Boston, MA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2507000 was used to isolate data for Boston, MA.|62.4|64.4||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|65.8|Las Vegas (Clark County), NV|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|65.2|66.4||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|70.5|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|69.8|71.2||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|73.7|Denver, CO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0820000 was used to isolate data for Denver, CO.|72.4|75.0||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|76.1|Seattle, WA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 5363000 was used to isolate data for Seattle, WA.|74.8|77.4||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|78.3|Philadelphia, PA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|77.5|79.1||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|79.0|U.S. Total, U.S. Total|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home|||78.9|79.1||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|79.5|Minneapolis, MN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|78.0|81.0||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|83.4|Washington, DC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 1150000 was used to isolate data for Washington, DC.|82.5|84.3||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|88.2|Cleveland, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3916000 was used to isolate data for Cleveland, OH.|87.3|89.1||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|89.2|Kansas City, MO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2938000 was used to isolate data for Kansas City, MO.|88.1|90.3||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|90.1|Detroit, MI|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2622000 was used to isolate data for Detroit, MI.|89.4|90.8||
Demographics|Percent Who Only Speak English at Home|2012|Both|All|90.5|Baltimore, MD|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2404000 was used to isolate data for Baltimore, MD.|89.7|91.3||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|28.4|Miami (Miami-Dade County), FL|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|27.8|29.0||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|39.8|Los Angeles, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|39.3|40.3||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|43.1|San Jose, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0668000 was used to isolate data for San Jose, CA.|42.0|44.2||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|51.0|New York City, NY|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3651000 was used to isolate data for New York City, NY.|50.6|51.4||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|53.3|Houston, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4835000 was used to isolate data for Houston, TX.|52.4|54.2||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|54.5|Long Beach, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0643000 was used to isolate data for Long Beach, CA.|52.4|56.6||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|55.5|San Antonio, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4865000 was used to isolate data for San Antonio, TX.|54.5|56.5||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|56.0|San Francisco, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0667000 was used to isolate data for San Francisco, CA.|54.9|57.1||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|58.8|Dallas, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48113 was used to isolate data for Dallas county, TX.|58.3|59.3||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|61.2|Oakland (Alameda County), CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0653000 was used to isolate data for Oakland, CA.|59.4|63.0||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|62.3|San Diego County, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 06073 was used to isolate data for San Diego County, CA.|61.6|63.0||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|63.6|Phoenix, AZ|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|62.3|64.9||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|63.9|Boston, MA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2507000 was used to isolate data for Boston, MA.|62.7|65.1||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|64.3|Chicago, Il|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|63.7|64.9||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|65.4|Las Vegas (Clark County), NV|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|64.8|66.0||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|72.4|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|71.7|73.1||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|73.6|Denver, CO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0820000 was used to isolate data for Denver, CO.|72.3|74.9||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|78.0|Philadelphia, PA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|77.2|78.8||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|78.2|Seattle, WA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 5363000 was used to isolate data for Seattle, WA.|76.8|79.6||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|79.2|U.S. Total, U.S. Total|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home|||79.1|79.3||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|80.0|Minneapolis, MN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|78.4|81.6||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|82.1|Washington, DC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 1150000 was used to isolate data for Washington, DC.|81.2|83.0||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|87.5|Kansas City, MO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2938000 was used to isolate data for Kansas City, MO.|86.3|88.7||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|88.2|Cleveland, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3916000 was used to isolate data for Cleveland, OH.|87.2|89.2||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|89.5|Detroit, MI|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2622000 was used to isolate data for Detroit, MI.|88.7|90.3||
Demographics|Percent Who Only Speak English at Home|2013|Both|All|91.3|Baltimore, MD|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2404000 was used to isolate data for Baltimore, MD.|90.6|92.0||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|27.7|Miami (Miami-Dade County), FL|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|27.1|28.3||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|39.8|Los Angeles, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|39.2|40.4||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|43.2|San Jose, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0668000 was used to isolate data for San Jose, CA.|42.0|44.4||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|51.0|New York City, NY|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3651000 was used to isolate data for New York City, NY.|50.7|51.3||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|51.8|Houston, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4835000 was used to isolate data for Houston, TX.|50.9|52.7||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|53.4|Long Beach, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0643000 was used to isolate data for Long Beach, CA.|51.4|55.4||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|55.8|San Antonio, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4865000 was used to isolate data for San Antonio, TX.|54.8|56.8||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|56.5|San Francisco, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0667000 was used to isolate data for San Francisco, CA.|55.5|57.5||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|57.6|Dallas, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48113 was used to isolate data for Dallas county, TX.|57.1|58.1||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|57.9|Oakland (Alameda County), CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0653000 was used to isolate data for Oakland, CA.|56.2|59.6||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|62.7|Boston, MA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2507000 was used to isolate data for Boston, MA.|61.2|64.2||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|62.8|Phoenix, AZ|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|61.8|63.8||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|63.1|San Diego County, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 06073 was used to isolate data for San Diego County, CA.|62.6|63.6||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|64.3|Chicago, Il|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|63.7|64.9||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|65.4|Las Vegas (Clark County), NV|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|64.8|66.0||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|68.4|Austin, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|71.3|Denver, CO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0820000 was used to isolate data for Denver, CO.|70.1|72.5||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|72.3|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|71.7|72.9||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|77.4|Philadelphia, PA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|76.8|78.0||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|77.9|Minneapolis, MN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|76.0|79.8||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|78.1|Seattle, WA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 5363000 was used to isolate data for Seattle, WA.|76.9|79.3||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|78.9|U.S. Total, U.S. Total|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home|||78.8|79.0||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|79.2|Portland (Multnomah County), OR|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|81.3|Charlotte, NC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|82.2|Washington, DC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 1150000 was used to isolate data for Washington, DC.|81.3|83.1||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|87.4|Indianapolis (Marion County), IN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|87.7|Cleveland, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3916000 was used to isolate data for Cleveland, OH.|86.7|88.7||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|87.9|Columbus, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|88.1|Kansas City, MO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2938000 was used to isolate data for Kansas City, MO.|86.9|89.3||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|89.8|Detroit, MI|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2622000 was used to isolate data for Detroit, MI.|88.8|90.8||
Demographics|Percent Who Only Speak English at Home|2014|Both|All|91.1|Baltimore, MD|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2404000 was used to isolate data for Baltimore, MD.|90.2|92.0||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|25.2|Miami (Miami-Dade County), FL|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|40.1|Los Angeles, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|42.8|San Jose, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|50.6|New York City, NY|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|50.7|Houston, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|52.0|Long Beach, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|56.0|San Antonio, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|56.1|San Francisco, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|57.1|Dallas, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|59.7|Oakland (Alameda County), CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|61.9|San Diego County, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|62.3|Boston, MA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|62.4|Phoenix, AZ|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|63.6|Chicago, Il|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|65.9|Las Vegas (Clark County), NV|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|68.3|Austin, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|71.7|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|72.2|Denver, CO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|76.8|Minneapolis, MN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|77.7|Philadelphia, PA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|78.5|U.S. Total, U.S. Total|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|79.5|Seattle, WA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|80.9|Portland (Multnomah County), OR|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|81.1|Charlotte, NC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|82.6|Washington, DC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|87.3|Columbus, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|87.4|Indianapolis (Marion County), IN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|87.7|Cleveland, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|88.6|Detroit, MI|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|88.6|Kansas City, MO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Who Only Speak English at Home|2015|Both|All|90.6|Baltimore, MD|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|25.6|Miami (Miami-Dade County), FL|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|40.0|Los Angeles, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|43.1|San Jose, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|50.3|Houston, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|50.8|New York City, NY|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|53.0|Long Beach, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|56.2|San Antonio, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|56.3|Dallas, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|56.6|San Francisco, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|58.2|Oakland (Alameda County), CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|61.2|Boston, MA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|61.2|San Diego County, CA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|61.9|Phoenix, AZ|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|63.3|Chicago, Il|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|65.3|Las Vegas (Clark County), NV|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|69.5|Austin, TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|70.3|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|74.1|Denver, CO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|76.7|Philadelphia, PA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|78.4|U.S. Total, U.S. Total|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|78.6|Minneapolis, MN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|79.3|Seattle, WA|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|79.9|Charlotte, NC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|80.4|Portland (Multnomah County), OR|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|82.9|Washington, DC|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|86.0|Indianapolis (Marion County), IN|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|86.2|Cleveland, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|86.8|Columbus, OH|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|87.4|Kansas City, MO|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|89.1|Detroit, MI|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Who Only Speak English at Home|2016|Both|All|90.3|Baltimore, MD|Percent of population (5 years and over) only speaking English at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID  S1601 - Language Spoken at Home||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|4.2|Baltimore, MD|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2404000 was used to isolate data for Baltimore, MD.|3.8|4.6||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|5.1|Seattle, WA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 5363000 was used to isolate data for Seattle, WA.|4.3|5.9||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|5.8|Kansas City, MO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2938000 was used to isolate data for Kansas City, MO.|4.9|6.7||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|6.3|Detroit, MI|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2622000 was used to isolate data for Detroit, MI.|5.8|6.8||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|7.4|Cleveland, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3916000 was used to isolate data for Cleveland, OH.|6.7|8.1||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|8.0|Washington, DC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 1150000 was used to isolate data for Washington, DC.|7.4|8.6||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|8.6|Minneapolis, MN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.6|9.6||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|10.2|Philadelphia, PA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.8|10.6||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|11.6|San Francisco, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0667000 was used to isolate data for San Francisco, CA.|11.1|12.1||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|13.0|U.S. Total, U.S. Total|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole|12.9|13.1||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|15.9|Boston, MA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2507000 was used to isolate data for Boston, MA.|15.3|16.5||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|19.9|Denver, CO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0820000 was used to isolate data for Denver, CO.|18.8|21.0||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|22.1|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|21.6|22.6||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|23.4|Oakland (Alameda County), CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0653000 was used to isolate data for Oakland, CA.|21.7|25.1||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|23.7|Las Vegas (Clark County), NV|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|23.3|24.1||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|23.9|San Jose, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0668000 was used to isolate data for San Jose, CA.|22.8|25.0||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|24.6|Chicago, Il|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|24.0|25.2||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|24.6|New York City, NY|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3651000 was used to isolate data for New York City, NY.|24.4|24.8||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|24.9|San Diego County, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 06073 was used to isolate data for San Diego County, CA.|24.5|25.3||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|31.8|Phoenix, AZ|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|30.9|32.7||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|32.4|Long Beach, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0643000 was used to isolate data for Long Beach, CA.|30.2|34.6||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|34.2|Dallas, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48113 was used to isolate data for Dallas county, TX.|33.7|34.7||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|38.2|Houston, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4835000 was used to isolate data for Houston, TX.|37.3|39.1||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|41.3|San Antonio, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4865000 was used to isolate data for San Antonio, TX.|40.4|42.2||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|42.8|Los Angeles, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|42.3|43.3||
Demographics|Percent Who Speak Spanish at Home|2012|Both|All|62.8|Miami (Miami-Dade County), FL|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|62.3|63.3||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|3.6|Baltimore, MD|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2404000 was used to isolate data for Baltimore, MD.|3.2|4.0||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|4.5|Seattle, WA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 5363000 was used to isolate data for Seattle, WA.|3.8|5.2||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|6.8|Detroit, MI|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2622000 was used to isolate data for Detroit, MI.|6.2|7.4||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|7.5|Minneapolis, MN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.5|8.5||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|7.8|Kansas City, MO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2938000 was used to isolate data for Kansas City, MO.|6.9|8.7||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|7.9|Cleveland, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3916000 was used to isolate data for Cleveland, OH.|7.0|8.8||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|8.8|Washington, DC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 1150000 was used to isolate data for Washington, DC.|8.2|9.4||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|10.1|Philadelphia, PA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.7|10.5||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|11.1|San Francisco, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0667000 was used to isolate data for San Francisco, CA.|10.5|11.7||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|13.0|U.S. Total, U.S. Total|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole|12.9|13.1||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|16.9|Boston, MA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2507000 was used to isolate data for Boston, MA.|16.2|17.6||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|20.4|Denver, CO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0820000 was used to isolate data for Denver, CO.|19.3|21.5||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|21.0|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|20.4|21.6||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|21.0|Oakland (Alameda County), CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0653000 was used to isolate data for Oakland, CA.|19.5|22.5||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|23.3|Las Vegas (Clark County), NV|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|22.8|23.8||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|24.2|San Jose, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0668000 was used to isolate data for San Jose, CA.|23.0|25.4||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|24.4|Chicago, Il|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|23.8|25.0||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|24.7|New York City, NY|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3651000 was used to isolate data for New York City, NY.|24.5|24.9||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|24.8|San Diego County, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 06073 was used to isolate data for San Diego County, CA.|24.3|25.3||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|30.4|Phoenix, AZ|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|29.4|31.4||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|33.6|Long Beach, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0643000 was used to isolate data for Long Beach, CA.|31.5|35.7||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|34.4|Dallas, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48113 was used to isolate data for Dallas county, TX.|34.0|34.8||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|37.6|Houston, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4835000 was used to isolate data for Houston, TX.|36.7|38.5||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|40.6|San Antonio, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4865000 was used to isolate data for San Antonio, TX.|39.7|41.5||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|43.4|Los Angeles, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|42.9|43.9||
Demographics|Percent Who Speak Spanish at Home|2013|Both|All|63.4|Miami (Miami-Dade County), FL|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|63.0|63.8||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|3.6|Columbus, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|4.1|Baltimore, MD|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2404000 was used to isolate data for Baltimore, MD.|3.6|4.6||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|4.1|Seattle, WA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 5363000 was used to isolate data for Seattle, WA.|3.4|4.8||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|6.5|Detroit, MI|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2622000 was used to isolate data for Detroit, MI.|5.9|7.1||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|6.7|Kansas City, MO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2938000 was used to isolate data for Kansas City, MO.|5.8|7.6||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|7.7|Cleveland, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3916000 was used to isolate data for Cleveland, OH.|7.0|8.4||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|8.3|Indianapolis (Marion County), IN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|8.4|Portland (Multnomah County), OR|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|8.6|Washington, DC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 1150000 was used to isolate data for Washington, DC.|8.0|9.2||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|10.7|Philadelphia, PA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|10.4|11.0||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|10.8|San Francisco, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0667000 was used to isolate data for San Francisco, CA.|10.3|11.3||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|10.9|Charlotte, NC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|13.1|U.S. Total, U.S. Total|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole|13.0|13.2||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|16.3|Boston, MA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2507000 was used to isolate data for Boston, MA.|15.6|17.0||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|20.9|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|20.4|21.4||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|23.4|Oakland (Alameda County), CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0653000 was used to isolate data for Oakland, CA.|21.8|25.0||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|23.5|San Jose, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0668000 was used to isolate data for San Jose, CA.|22.4|24.6||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|23.7|Las Vegas (Clark County), NV|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|23.2|24.2||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|24.2|San Diego County, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 06073 was used to isolate data for San Diego County, CA.|23.7|24.7||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|24.4|Austin, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|24.5|New York City, NY|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3651000 was used to isolate data for New York City, NY.|24.3|24.7||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|24.6|Chicago, Il|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|24.0|25.2||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|30.6|Phoenix, AZ|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|29.7|31.5||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|33.5|Long Beach, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0643000 was used to isolate data for Long Beach, CA.|31.6|35.4||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|35.0|Dallas, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48113 was used to isolate data for Dallas county, TX.|34.6|35.4||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|38.3|Houston, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4835000 was used to isolate data for Houston, TX.|37.3|39.3||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|40.7|San Antonio, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4865000 was used to isolate data for San Antonio, TX.|39.7|41.7||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|42.9|Los Angeles, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|42.3|43.5||
Demographics|Percent Who Speak Spanish at Home|2014|Both|All|64.0|Miami (Miami-Dade County), FL|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|63.6|64.4||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|3.7|Baltimore, MD|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|3.9|Columbus, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|4.1|Seattle, WA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|6.8|Kansas City, MO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|7.2|Detroit, MI|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|7.9|Cleveland, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|8.2|Indianapolis (Marion County), IN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|8.2|Portland (Multnomah County), OR|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|8.5|Minneapolis, MN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|8.9|Washington, DC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|9.9|Philadelphia, PA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|11.0|San Francisco, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|11.1|Charlotte, NC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|13.3|U.S. Total, U.S. Total|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|16.6|Boston, MA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|21.0|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|21.3|Denver, CO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|22.3|Oakland (Alameda County), CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|23.1|San Jose, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|23.5|Las Vegas (Clark County), NV|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|23.9|Austin, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|24.6|New York City, NY|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|24.7|Chicago, Il|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|25.2|San Diego County, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|31.4|Phoenix, AZ|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|34.9|Dallas, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|36.6|Long Beach, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|38.9|Houston, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|40.1|San Antonio, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|42.8|Los Angeles, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Who Speak Spanish at Home|2015|Both|All|65.7|Miami (Miami-Dade County), FL|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|3.5|Columbus, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|3.8|Seattle, WA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|4.3|Baltimore, MD|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|6.5|Kansas City, MO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|7.4|Minneapolis, MN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|8.0|Portland (Multnomah County), OR|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|8.4|Cleveland, OH|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|9.2|Indianapolis (Marion County), IN|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|9.2|Washington, DC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|10.5|Philadelphia, PA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|10.9|Charlotte, NC|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|10.9|San Francisco, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|13.3|U.S. Total, U.S. Total|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|17.1|Boston, MA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|19.4|Denver, CO|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|21.7|Fort Worth (Tarrant County), TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|21.8|Oakland (Alameda County), CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|23.0|Austin, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|23.0|San Jose, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|24.2|Las Vegas (Clark County), NV|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|24.3|New York City, NY|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|25.1|Chicago, Il|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|25.5|San Diego County, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|31.7|Phoenix, AZ|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|35.1|Dallas, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|35.3|Long Beach, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|39.0|Houston, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|39.8|San Antonio, TX|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|43.0|Los Angeles, CA|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Percent Who Speak Spanish at Home|2016|Both|All|65.9|Miami (Miami-Dade County), FL|Percent of population (5 years and over) speaking Spanish (or Spanish Creole) at home using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1601 - Language Spoken at Home||Spanish or Spanish Creole; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.1|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.1|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.1|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.1|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.2|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.3|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.3|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.3|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.4|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|0.3|0.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|0.1|0.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.4|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|0.2|0.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.4|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|0.3|0.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|0.4|0.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.5|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|0.3|0.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.7|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|0.5|0.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|0.7|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||0.6|0.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|1.1|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|0.8|1.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|American Indian/Alaska Native|1.6|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1.3|1.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|1.1|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|0.8|1.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|1.5|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|1.4|1.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|1.9|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.|1.5|2.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|2.2|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|2.0|2.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|2.3|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|1.9|2.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|2.5|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|2.4|2.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|3.2|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|2.9|3.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|3.4|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|3.0|3.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|3.4|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|3.1|3.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|4.8|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.6|5.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|4.9|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone|4.8|5.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|5.3|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|5.2|5.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|5.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.9|6.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|5.9|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.6|6.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|6.4|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|6.2|6.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|6.5|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|6.1|6.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|8.8|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.6|9.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|9.1|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|8.8|9.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|11.2|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|11.0|11.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|11.3|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|11.0|11.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|11.8|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|10.5|13.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|13.0|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|12.9|13.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|14.6|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|13.5|15.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|16.1|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|14.8|17.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Asian/PI|32.9|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|32.5|33.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|2.8|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|2.5|3.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|4.8|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|4.7|4.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|5.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.2|5.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|6.5|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.1|6.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|6.6|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|6.1|7.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|7.1|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.3|7.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|9.0|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.7|9.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|9.8|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|9.6|10.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|10.3|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.1|10.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|12.3|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||12.2|12.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|12.8|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.5|14.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|15.0|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|14.9|15.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|17.1|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|17.0|17.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|17.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|16.4|19.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|22.0|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|21.9|22.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|22.6|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|22.5|22.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|23.2|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|22.6|23.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|23.3|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|22.9|23.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|25.2|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|23.8|26.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|30.0|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|28.9|31.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|31.5|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|31.0|32.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|41.6|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|41.3|41.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|48.8|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|48.6|49.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|52.8|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|51.6|54.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|62.7|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|62.3|63.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Black|81.4|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|80.7|82.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|4.4|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|7.5|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|7.0|8.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|9.9|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|10.0|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|9.2|10.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|10.5|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|9.4|11.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|10.7|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|10.0|11.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|13.0|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|15.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|16.9|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||16.8|17.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|18.6|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|18.0|19.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|26.8|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|25.0|28.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|27.4|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|28.9|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|29.0|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|28.5|29.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|29.8|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|31.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|32.7|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|33.2|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|32.1|34.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|38.9|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|40.3|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|39.4|41.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|41.8|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|39.6|44.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|43.4|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|42.5|44.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|48.4|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|48.0|48.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|63.6|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|63.0|64.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Hispanic|64.3|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|0.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|0.5|0.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.2|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|1.0|1.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.3|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|1.1|1.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.4|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|1.2|1.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.4|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|1.2|1.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.6|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|1.3|1.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.6|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|1.5|1.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.7|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|1.4|2.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.8|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|1.6|2.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|1.8|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1.5|2.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.0|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|1.6|2.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.0|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|1.5|2.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.1|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|1.7|2.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.1|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||2.0|2.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.1|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|1.7|2.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.2|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|2.0|2.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|1.9|2.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.5|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|2.1|2.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.8|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|2.6|3.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|2.9|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|2.6|3.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|3.2|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|2.9|3.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|3.7|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|3.3|4.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|4.1|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|3.3|4.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|4.2|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|3.4|5.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|4.4|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|3.6|5.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|Multiracial|4.9|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.3|5.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|8.2|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|7.6|8.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|16.1|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|16.0|16.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|25.3|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|24.6|26.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|26.0|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|25.5|26.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|26.6|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|25.1|28.1||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|27.6|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|26.8|28.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|28.1|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|28.0|28.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|28.2|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|26.4|30.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|28.4|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|28.0|28.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|31.8|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|31.3|32.3||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|32.1|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|32.0|32.2||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|32.3|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|31.1|33.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|32.8|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|32.7|32.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|35.3|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|35.2|35.4||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|36.4|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|36.3|36.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|41.5|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|41.4|41.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|45.9|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|45.0|46.8||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|46.0|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|45.4|46.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|46.6|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|46.5|46.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|47.5|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|47.4|47.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|50.5|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|50.4|50.6||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|52.6|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|52.5|52.7||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|54.4|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|53.3|55.5||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|60.5|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|59.0|62.0||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|62.8|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||62.7|62.9||
Demographics|Race/Ethnicity (Percent)|2012|Both|White|65.2|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|63.8|66.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.1|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.2|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.3|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.3|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.3|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.3|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.3|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|0.1|0.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.4|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|0.3|0.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.4|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|0.2|0.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.4|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|0.3|0.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|0.4|0.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.7|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||0.6|0.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|0.9|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|0.6|1.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|American Indian/Alaska Native|1.7|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1.4|2.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|1.3|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|0.9|1.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|1.4|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.|1.1|1.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|1.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|1.5|1.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|2.4|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|2.2|2.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|2.6|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|2.2|3.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|2.6|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|2.4|2.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|2.9|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|2.6|3.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|3.2|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|3.0|3.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|3.4|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|3.2|3.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|4.6|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.4|4.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|5.0|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone|4.9|5.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|5.5|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|5.4|5.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|6.0|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.7|6.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|6.2|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|5.2|7.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|6.3|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|5.9|6.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|6.7|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|6.6|6.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|8.9|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|8.5|9.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|9.1|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.9|9.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|11.1|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|10.9|11.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|11.2|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.8|11.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|12.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.0|13.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|13.3|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|12.3|14.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|13.4|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|13.3|13.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|16.1|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|14.6|17.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|32.9|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|32.0|33.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Asian/PI|33.3|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|32.9|33.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|3.0|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|2.8|3.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|4.8|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|4.7|4.9||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|5.5|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.4|5.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|6.6|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|6.1|7.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|6.8|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.4|7.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|6.9|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.1|7.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|8.6|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.3|8.9||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|9.4|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|9.1|9.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|10.4|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.2|10.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|12.3|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||12.2|12.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|12.9|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.6|14.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|14.9|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|14.7|15.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|16.8|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|16.7|16.9||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|17.9|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|16.4|19.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|21.8|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|21.6|22.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|22.0|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|21.4|22.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|22.4|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|22.3|22.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|22.6|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|21.9|23.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|24.8|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|23.4|26.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|27.8|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|26.6|29.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|31.4|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|31.0|31.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|41.8|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|41.6|42.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|48.0|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|47.6|48.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|51.2|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|50.0|52.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|62.5|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|62.2|62.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Black|80.1|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|79.3|80.9||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|4.6|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|6.4|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.5|7.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|7.7|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|7.1|8.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|9.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.7|10.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|10.1|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|10.4|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|9.5|11.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|10.8|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|10.1|11.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|13.3|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|17.1|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||17.0|17.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|18.8|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|18.1|19.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|26.6|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|24.8|28.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|27.6|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|28.8|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|28.3|29.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|28.9|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|30.0|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|30.9|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|32.9|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|33.5|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|32.5|34.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|39.0|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|41.3|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|40.2|42.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|42.1|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|40.0|44.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|43.7|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|42.9|44.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|49.3|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|48.8|49.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|63.0|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|62.4|63.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Hispanic|65.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|0.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|0.5|0.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.1|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|0.9|1.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.3|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|1.1|1.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.5|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|1.4|1.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.5|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|1.2|1.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.6|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|1.5|1.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.7|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|1.5|1.9||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.7|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|1.4|2.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|1.8|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|1.5|2.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.1|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|2.0|2.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.2|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1.9|2.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.2|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||2.1|2.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.3|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|2.0|2.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.5|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|2.0|3.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.5|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|2.1|2.9||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.6|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|2.2|3.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.6|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|2.0|3.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.6|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|2.1|3.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.7|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|2.2|3.2||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|2.8|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|2.5|3.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|3.1|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|2.9|3.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|3.3|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|3.0|3.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|3.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|3.1|3.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|5.0|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.0|6.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|5.1|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|4.4|5.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|Multiracial|5.4|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.8|6.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|8.9|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|8.2|9.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|15.0|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|14.9|15.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|25.8|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|25.2|26.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|26.1|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|25.6|26.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|26.2|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|24.8|27.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|27.3|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|26.5|28.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|28.2|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|28.1|28.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|28.2|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|27.8|28.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|28.7|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|27.1|30.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|31.6|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|31.5|31.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|32.0|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|31.4|32.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|32.6|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|32.5|32.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|33.8|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|32.5|35.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|35.6|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|35.5|35.7||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|36.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|36.1|36.3||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|41.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|41.2|41.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|45.1|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|44.1|46.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|45.9|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|45.2|46.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|45.9|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|45.8|46.0||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|47.0|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|46.9|47.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|50.0|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|49.9|50.1||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|53.3|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|53.2|53.4||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|55.5|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|54.2|56.8||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|60.0|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|58.4|61.6||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|62.4|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||62.3|62.5||
Demographics|Race/Ethnicity (Percent)|2013|Both|White|67.0|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|66.0|68.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.1|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.1|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.1|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.1|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|0.0|0.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.2|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|0.1|0.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.3|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|0.1|0.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.3|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|0.2|0.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.4|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|0.3|0.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.4|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|0.3|0.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|0.3|0.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.6|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|0.4|0.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.7|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|0.5|0.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.7|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||0.6|0.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|0.9|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|1.2|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|0.8|1.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|American Indian/Alaska Native|1.6|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1.3|1.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|1.2|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|0.8|1.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|1.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|1.5|1.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|1.9|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.|1.4|2.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|2.3|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|2.0|2.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|2.6|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|2.7|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|2.6|2.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|2.7|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|2.5|2.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|3.6|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|3.2|4.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|3.7|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|3.5|3.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|3.7|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|3.5|3.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|4.5|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|4.9|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.8|5.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|5.2|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone|5.1|5.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|5.3|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|5.6|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.8|6.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|5.7|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|5.6|5.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|6.0|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.6|6.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|6.3|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|6.8|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|6.9|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|6.5|7.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|7.0|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|6.9|7.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|9.2|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|9.0|9.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|9.7|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|9.5|9.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|11.4|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|11.2|11.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|11.5|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|11.1|11.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|13.7|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|13.6|13.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|13.9|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|13.0|14.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|14.1|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|12.7|15.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|15.3|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|14.2|16.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|33.7|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|33.4|34.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Asian/PI|34.2|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|33.3|35.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|2.8|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|2.5|3.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|4.6|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|4.4|4.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|5.1|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|5.2|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.0|5.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|6.4|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|5.9|6.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|6.6|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.2|7.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|6.9|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.1|7.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|8.4|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|8.6|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.3|8.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|9.3|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|8.9|9.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|10.6|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.5|10.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|12.3|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||12.2|12.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|12.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.1|13.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|15.4|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|15.2|15.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|16.7|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|16.6|16.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|19.3|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|17.9|20.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|21.4|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|21.8|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|21.6|22.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|22.3|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|21.7|22.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|22.3|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|22.2|22.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|22.8|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|22.2|23.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|23.6|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|22.6|24.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|27.1|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|29.5|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|28.3|30.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|30.7|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|31.0|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|30.6|31.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|41.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|40.9|41.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|47.7|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|47.5|47.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|52.6|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|51.2|54.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|62.1|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|61.8|62.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Black|79.1|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|78.1|80.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|4.7|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|5.1|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|6.2|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.4|7.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|7.2|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|6.6|7.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|9.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.4|11.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|9.8|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|10.0|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|9.1|10.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|10.4|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|10.7|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|9.9|11.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|11.2|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|12.7|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|13.6|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|15.3|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|17.3|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||17.2|17.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|18.6|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|18.0|19.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|27.4|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|25.8|29.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|27.8|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|29.0|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|29.5|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|29.0|30.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|30.3|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|30.8|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|32.8|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|32.0|33.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|33.2|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|33.9|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|39.3|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|41.2|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|40.2|42.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|42.3|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|40.3|44.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|44.1|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|43.2|45.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|48.6|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|48.0|49.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|63.8|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|63.2|64.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|Hispanic|66.2|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|0.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|0.5|0.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.1|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|0.9|1.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.3|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|1.1|1.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.5|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|1.3|1.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.7|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|1.4|2.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.7|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|1.6|1.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.9|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|1.6|2.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.9|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|1.7|2.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.9|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|1.6|2.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|1.9|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1.6|2.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.0|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|1.7|2.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.1|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|1.9|2.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.1|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|1.9|2.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.2|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|1.8|2.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.2|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||2.1|2.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.3|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|1.9|2.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|1.9|2.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.6|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|2.1|3.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.6|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.6|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|2.1|3.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.7|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|2.8|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|2.5|3.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|3.1|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|3.3|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|3.1|3.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|3.6|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|3.4|3.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|3.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|3.1|4.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|3.7|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|3.4|4.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|3.9|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|4.4|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|3.8|5.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|5.4|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.8|6.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|Multiracial|5.6|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|10.2|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|9.4|11.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|14.7|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|14.6|14.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|24.6|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|24.0|25.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|25.3|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|24.7|25.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|26.7|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|25.9|27.5||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|27.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|25.9|28.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|27.5|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|26.2|28.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|28.0|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|27.9|28.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|28.5|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|28.1|28.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|31.0|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|30.9|31.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|31.8|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|31.2|32.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|32.0|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|30.6|33.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|32.3|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|32.2|32.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|35.7|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|35.6|35.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|35.7|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|35.6|35.8||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|40.8|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|40.6|41.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|44.9|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|43.9|45.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|45.1|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|45.0|45.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|45.6|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|45.0|46.2||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|46.6|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|46.5|46.7||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|49.2|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|49.1|49.3||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|53.3|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|53.2|53.4||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|55.0|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|53.9|56.1||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|55.8|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|60.3|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|58.7|61.9||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|61.9|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||61.8|62.0||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|63.9|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|66.2|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|64.8|67.6||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|68.8|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|77.1|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2014|Both|White|77.5|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.5|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.5|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.6|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.7|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.8|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.8|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.8|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.8|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.8|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.9|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|0.9|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|1.0|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|1.8|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|American Indian/Alaska Native|2.0|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|1.3|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|1.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|2.1|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|2.7|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|2.9|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|2.9|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|2.9|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|3.3|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|3.4|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|3.9|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|4.6|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|5.2|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|5.4|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|5.5|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|5.9|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|6.2|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|6.4|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|6.6|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|6.9|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|7.0|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|7.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|9.5|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|9.7|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|11.5|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|11.7|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|12.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|14.1|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|14.1|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|15.3|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|34.8|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Asian/PI|34.9|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|3.1|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|5.1|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|5.2|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|5.4|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|7.1|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|7.2|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|7.3|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|8.5|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|9.2|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|9.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|11.0|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|12.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|12.7|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|16.0|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|18.4|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|19.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|21.7|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|22.3|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|22.3|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|24.0|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|24.9|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|25.3|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|27.2|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|29.7|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|31.0|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|31.1|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|42.4|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|47.4|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|50.7|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|61.6|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Black|79.5|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|4.8|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|5.2|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|6.3|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|8.0|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|9.7|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|9.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|10.0|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|10.5|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|10.6|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|11.3|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|12.8|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|14.0|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|15.3|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|17.6|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|19.5|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|27.2|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|28.2|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|28.9|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|29.1|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|30.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|30.6|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|32.3|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|33.4|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|33.9|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|39.5|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|42.9|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|43.8|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|44.7|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|48.8|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|63.8|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Hispanic|66.8|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|1.5|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|1.8|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|1.9|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.5|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.6|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.6|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.7|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.7|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.9|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.9|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|2.9|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.0|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.0|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.1|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.2|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.3|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.3|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.3|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.4|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.5|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|3.7|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|4.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|4.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|4.5|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|4.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|4.8|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|4.9|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|5.2|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|5.4|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|6.2|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|Multiracial|6.2|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|14.1|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|31.0|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|36.8|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|39.1|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|40.0|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|40.3|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|41.9|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|42.6|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|47.3|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|48.4|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|52.1|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|52.7|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|55.3|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|56.8|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|58.8|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|59.8|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|62.2|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|62.3|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|63.4|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|63.8|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|68.2|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|69.1|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|69.7|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|70.8|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|71.6|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|73.1|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|74.1|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|75.5|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|77.1|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|78.9|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2015|Both|White|81.9|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.5|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.6|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.7|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.7|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.7|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.8|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.8|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.8|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|0.8|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|1.0|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|1.5|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|American Indian/Alaska Native|2.0|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|1.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|1.8|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|2.1|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|2.8|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|3.0|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|3.1|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|3.7|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|3.9|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|3.9|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|5.0|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|5.4|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|5.4|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|5.6|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|6.1|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|6.4|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|6.6|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|7.0|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|7.1|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|7.4|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|9.8|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|9.8|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|11.4|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|11.7|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|12.5|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|14.1|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|14.9|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|16.4|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|34.5|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Asian/PI|34.9|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|3.0|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|5.0|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|5.0|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|5.7|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|6.5|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|7.1|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|7.4|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|8.3|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|8.8|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|9.8|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|11.5|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|12.6|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|12.7|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|15.9|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|17.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|18.1|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|22.2|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|22.6|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|22.6|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|23.5|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|24.3|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|25.8|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|27.6|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|28.5|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|29.7|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|31.7|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|42.2|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|47.1|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|49.6|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|62.5|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Black|79.1|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|5.1|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|5.3|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|6.4|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|7.0|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|8.8|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|10.2|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|10.9|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|10.9|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|11.0|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|11.4|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|13.0|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|14.4|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|15.2|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|17.8|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|19.1|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|26.1|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|28.4|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|29.2|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|29.7|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|30.2|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|30.9|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|32.1|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|33.5|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|33.8|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|39.9|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|43.8|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|44.5|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|44.8|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|48.9|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|64.0|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Hispanic|67.7|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|1.6|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|1.7|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.2|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.3|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.4|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.5|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.6|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.6|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|2.7|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.0|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.2|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.3|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.3|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.4|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.5|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.5|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.6|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.6|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.8|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|3.8|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|4.2|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|4.7|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.1|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.2|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.2|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.3|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|5.7|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|7.2|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|Multiracial|7.5|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|13.6|Detroit, MI|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|30.8|Baltimore, MD|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|34.6|Oakland (Alameda County), CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|39.1|San Jose, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|40.0|Cleveland, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|40.4|Philadelphia, PA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|40.7|Washington, DC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|42.5|New York City, NY|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|46.4|San Francisco, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|48.4|Chicago, Il|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|50.4|Long Beach, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|52.3|Los Angeles, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|53.2|Boston, MA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|54.5|Charlotte, NC|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|57.2|Houston, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|61.4|Indianapolis (Marion County), IN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|61.6|Kansas City, MO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|61.7|Las Vegas (Clark County), NV|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|61.8|Dallas, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|64.3|Minneapolis, MN|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|67.5|Seattle, WA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|67.6|Columbus, OH|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|67.8|Fort Worth (Tarrant County), TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|67.8|Phoenix, AZ|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|71.9|San Diego County, CA|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|72.6|U.S. Total, U.S. Total|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|74.5|Austin, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|74.5|Miami (Miami-Dade County), FL|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|75.4|Denver, CO|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|78.8|Portland (Multnomah County), OR|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Race/Ethnicity (Percent)|2016|Both|White|79.4|San Antonio, TX|Percentage of race/ethnicity population distribution using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Sex (Percent)|2012|Female|All|49.2|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|49.1|49.3||
Demographics|Sex (Percent)|2012|Female|All|49.7|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|49.6|49.8||
Demographics|Sex (Percent)|2012|Female|All|49.7|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|49.6|49.8||
Demographics|Sex (Percent)|2012|Female|All|49.7|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|49.3|50.1||
Demographics|Sex (Percent)|2012|Female|All|49.8|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|49.5|50.1||
Demographics|Sex (Percent)|2012|Female|All|49.8|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|48.9|50.7||
Demographics|Sex (Percent)|2012|Female|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|49.9|50.1||
Demographics|Sex (Percent)|2012|Female|All|50.1|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|49.7|50.5||
Demographics|Sex (Percent)|2012|Female|All|50.3|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|50.0|50.6||
Demographics|Sex (Percent)|2012|Female|All|50.5|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|50.4|50.6||
Demographics|Sex (Percent)|2012|Female|All|50.6|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|49.9|51.3||
Demographics|Sex (Percent)|2012|Female|All|50.7|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|49.7|51.7||
Demographics|Sex (Percent)|2012|Female|All|50.8|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||50.7|50.9||
Demographics|Sex (Percent)|2012|Female|All|50.9|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|50.8|51.0||
Demographics|Sex (Percent)|2012|Female|All|51.3|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|51.0|51.6||
Demographics|Sex (Percent)|2012|Female|All|51.4|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|51.3|51.5||
Demographics|Sex (Percent)|2012|Female|All|51.4|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|50.5|52.3||
Demographics|Sex (Percent)|2012|Female|All|51.5|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|51.2|51.8||
Demographics|Sex (Percent)|2012|Female|All|51.9|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|51.6|52.2||
Demographics|Sex (Percent)|2012|Female|All|52.1|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|51.5|52.7||
Demographics|Sex (Percent)|2012|Female|All|52.4|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|51.7|53.1||
Demographics|Sex (Percent)|2012|Female|All|52.4|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|52.3|52.5||
Demographics|Sex (Percent)|2012|Female|All|52.7|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|52.2|53.2||
Demographics|Sex (Percent)|2012|Female|All|52.7|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|52.6|52.8||
Demographics|Sex (Percent)|2012|Female|All|52.7|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|52.6|52.8||
Demographics|Sex (Percent)|2012|Female|All|52.9|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|52.8|53.0||
Demographics|Sex (Percent)|2012|Male|All|47.1|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|47.0|47.2||
Demographics|Sex (Percent)|2012|Male|All|47.3|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|46.8|47.8||
Demographics|Sex (Percent)|2012|Male|All|47.3|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|47.2|47.4||
Demographics|Sex (Percent)|2012|Male|All|47.3|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|47.2|47.4||
Demographics|Sex (Percent)|2012|Male|All|47.6|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|46.9|48.3||
Demographics|Sex (Percent)|2012|Male|All|47.6|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|47.5|47.7||
Demographics|Sex (Percent)|2012|Male|All|47.9|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|47.3|48.5||
Demographics|Sex (Percent)|2012|Male|All|48.1|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|47.8|48.4||
Demographics|Sex (Percent)|2012|Male|All|48.5|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|48.2|48.8||
Demographics|Sex (Percent)|2012|Male|All|48.6|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|48.5|48.7||
Demographics|Sex (Percent)|2012|Male|All|48.6|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|47.7|49.5||
Demographics|Sex (Percent)|2012|Male|All|48.7|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|48.4|49.0||
Demographics|Sex (Percent)|2012|Male|All|49.1|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|49.0|49.2||
Demographics|Sex (Percent)|2012|Male|All|49.2|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||49.1|49.3||
Demographics|Sex (Percent)|2012|Male|All|49.3|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|48.3|50.3||
Demographics|Sex (Percent)|2012|Male|All|49.4|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|48.7|50.1||
Demographics|Sex (Percent)|2012|Male|All|49.5|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|49.4|49.6||
Demographics|Sex (Percent)|2012|Male|All|49.7|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|49.4|50.0||
Demographics|Sex (Percent)|2012|Male|All|49.9|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|49.5|50.3||
Demographics|Sex (Percent)|2012|Male|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|49.9|50.1||
Demographics|Sex (Percent)|2012|Male|All|50.2|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|49.9|50.5||
Demographics|Sex (Percent)|2012|Male|All|50.2|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|49.3|51.1||
Demographics|Sex (Percent)|2012|Male|All|50.3|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|50.2|50.4||
Demographics|Sex (Percent)|2012|Male|All|50.3|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|50.2|50.4||
Demographics|Sex (Percent)|2012|Male|All|50.3|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|49.9|50.7||
Demographics|Sex (Percent)|2012|Male|All|50.8|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|50.7|50.9||
Demographics|Sex (Percent)|2013|Female|All|49.1|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|49.0|49.2||
Demographics|Sex (Percent)|2013|Female|All|49.7|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|49.0|50.4||
Demographics|Sex (Percent)|2013|Female|All|49.7|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|49.6|49.8||
Demographics|Sex (Percent)|2013|Female|All|49.8|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|49.7|49.9||
Demographics|Sex (Percent)|2013|Female|All|49.8|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|49.3|50.3||
Demographics|Sex (Percent)|2013|Female|All|49.9|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|49.5|50.3||
Demographics|Sex (Percent)|2013|Female|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|49.9|50.1||
Demographics|Sex (Percent)|2013|Female|All|50.0|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|49.6|50.4||
Demographics|Sex (Percent)|2013|Female|All|50.0|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|49.4|50.6||
Demographics|Sex (Percent)|2013|Female|All|50.2|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|49.2|51.2||
Demographics|Sex (Percent)|2013|Female|All|50.2|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|49.3|51.1||
Demographics|Sex (Percent)|2013|Female|All|50.4|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|49.7|51.1||
Demographics|Sex (Percent)|2013|Female|All|50.4|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|50.2|50.6||
Demographics|Sex (Percent)|2013|Female|All|50.6|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|50.5|50.7||
Demographics|Sex (Percent)|2013|Female|All|50.8|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||50.7|50.9||
Demographics|Sex (Percent)|2013|Female|All|50.9|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|50.8|51.0||
Demographics|Sex (Percent)|2013|Female|All|50.9|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|50.6|51.2||
Demographics|Sex (Percent)|2013|Female|All|51.2|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|50.9|51.5||
Demographics|Sex (Percent)|2013|Female|All|51.4|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|51.3|51.5||
Demographics|Sex (Percent)|2013|Female|All|52.0|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|51.2|52.8||
Demographics|Sex (Percent)|2013|Female|All|52.2|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|51.8|52.6||
Demographics|Sex (Percent)|2013|Female|All|52.3|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|52.2|52.4||
Demographics|Sex (Percent)|2013|Female|All|52.6|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|52.0|53.2||
Demographics|Sex (Percent)|2013|Female|All|52.6|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|52.5|52.7||
Demographics|Sex (Percent)|2013|Female|All|52.7|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|52.6|52.8||
Demographics|Sex (Percent)|2013|Female|All|52.9|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|52.8|53.0||
Demographics|Sex (Percent)|2013|Male|All|47.1|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|47.0|47.2||
Demographics|Sex (Percent)|2013|Male|All|47.3|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|47.2|47.4||
Demographics|Sex (Percent)|2013|Male|All|47.4|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|46.8|48.0||
Demographics|Sex (Percent)|2013|Male|All|47.4|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|47.3|47.5||
Demographics|Sex (Percent)|2013|Male|All|47.7|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|47.6|47.8||
Demographics|Sex (Percent)|2013|Male|All|47.8|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|47.4|48.2||
Demographics|Sex (Percent)|2013|Male|All|48.0|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|47.2|48.8||
Demographics|Sex (Percent)|2013|Male|All|48.6|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|48.5|48.7||
Demographics|Sex (Percent)|2013|Male|All|48.8|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|48.5|49.1||
Demographics|Sex (Percent)|2013|Male|All|49.1|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|49.0|49.2||
Demographics|Sex (Percent)|2013|Male|All|49.1|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|48.8|49.4||
Demographics|Sex (Percent)|2013|Male|All|49.2|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||49.1|49.3||
Demographics|Sex (Percent)|2013|Male|All|49.4|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|49.3|49.5||
Demographics|Sex (Percent)|2013|Male|All|49.6|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|48.9|50.3||
Demographics|Sex (Percent)|2013|Male|All|49.6|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|49.4|49.8||
Demographics|Sex (Percent)|2013|Male|All|49.8|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|48.8|50.8||
Demographics|Sex (Percent)|2013|Male|All|49.8|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|48.9|50.7||
Demographics|Sex (Percent)|2013|Male|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|49.9|50.1||
Demographics|Sex (Percent)|2013|Male|All|50.0|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|49.6|50.4||
Demographics|Sex (Percent)|2013|Male|All|50.0|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|49.4|50.6||
Demographics|Sex (Percent)|2013|Male|All|50.1|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|49.7|50.5||
Demographics|Sex (Percent)|2013|Male|All|50.2|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|50.1|50.3||
Demographics|Sex (Percent)|2013|Male|All|50.2|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|49.7|50.7||
Demographics|Sex (Percent)|2013|Male|All|50.3|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|49.6|51.0||
Demographics|Sex (Percent)|2013|Male|All|50.3|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|50.2|50.4||
Demographics|Sex (Percent)|2013|Male|All|50.9|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|50.8|51.0||
Demographics|Sex (Percent)|2014|Female|All|49.1|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|49.0|49.2||
Demographics|Sex (Percent)|2014|Female|All|49.3|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|48.5|50.1||
Demographics|Sex (Percent)|2014|Female|All|49.4|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|48.6|50.2||
Demographics|Sex (Percent)|2014|Female|All|49.6|Austin, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Female|All|49.7|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|49.6|49.8||
Demographics|Sex (Percent)|2014|Female|All|49.9|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|49.5|50.3||
Demographics|Sex (Percent)|2014|Female|All|49.9|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|49.8|50.0||
Demographics|Sex (Percent)|2014|Female|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|49.9|50.1||
Demographics|Sex (Percent)|2014|Female|All|50.0|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|49.6|50.4||
Demographics|Sex (Percent)|2014|Female|All|50.3|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|50.0|50.6||
Demographics|Sex (Percent)|2014|Female|All|50.3|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|49.9|50.7||
Demographics|Sex (Percent)|2014|Female|All|50.4|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|49.3|51.5||
Demographics|Sex (Percent)|2014|Female|All|50.5|Portland (Multnomah County), OR|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Sex (Percent)|2014|Female|All|50.7|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|50.6|50.8||
Demographics|Sex (Percent)|2014|Female|All|50.7|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|50.4|51.0||
Demographics|Sex (Percent)|2014|Female|All|50.8|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||50.7|50.9||
Demographics|Sex (Percent)|2014|Female|All|51.1|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|51.0|51.2||
Demographics|Sex (Percent)|2014|Female|All|51.3|Columbus, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Female|All|51.4|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|50.7|52.1||
Demographics|Sex (Percent)|2014|Female|All|51.4|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|51.3|51.5||
Demographics|Sex (Percent)|2014|Female|All|51.7|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|51.4|52.0||
Demographics|Sex (Percent)|2014|Female|All|51.8|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|51.5|52.1||
Demographics|Sex (Percent)|2014|Female|All|51.8|Indianapolis (Marion County), IN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Female|All|51.9|Charlotte, NC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Female|All|52.0|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|51.2|52.8||
Demographics|Sex (Percent)|2014|Female|All|52.3|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|51.5|53.1||
Demographics|Sex (Percent)|2014|Female|All|52.3|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|52.2|52.4||
Demographics|Sex (Percent)|2014|Female|All|52.6|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|52.5|52.7||
Demographics|Sex (Percent)|2014|Female|All|52.7|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|52.6|52.8||
Demographics|Sex (Percent)|2014|Female|All|52.8|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|52.7|52.9||
Demographics|Sex (Percent)|2014|Female|All|52.9|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|52.4|53.4||
Demographics|Sex (Percent)|2014|Male|All|47.1|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|46.6|47.6||
Demographics|Sex (Percent)|2014|Male|All|47.2|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.|47.1|47.3||
Demographics|Sex (Percent)|2014|Male|All|47.3|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|47.2|47.4||
Demographics|Sex (Percent)|2014|Male|All|47.4|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.|47.3|47.5||
Demographics|Sex (Percent)|2014|Male|All|47.7|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|46.9|48.5||
Demographics|Sex (Percent)|2014|Male|All|47.7|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.|47.6|47.8||
Demographics|Sex (Percent)|2014|Male|All|48.0|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|47.2|48.8||
Demographics|Sex (Percent)|2014|Male|All|48.1|Charlotte, NC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Male|All|48.2|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|47.9|48.5||
Demographics|Sex (Percent)|2014|Male|All|48.2|Indianapolis (Marion County), IN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Male|All|48.3|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|48.0|48.6||
Demographics|Sex (Percent)|2014|Male|All|48.6|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|47.9|49.3||
Demographics|Sex (Percent)|2014|Male|All|48.6|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|48.5|48.7||
Demographics|Sex (Percent)|2014|Male|All|48.7|Columbus, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Male|All|48.9|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|48.8|49.0||
Demographics|Sex (Percent)|2014|Male|All|49.2|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates|||49.1|49.3||
Demographics|Sex (Percent)|2014|Male|All|49.3|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.|49.2|49.4||
Demographics|Sex (Percent)|2014|Male|All|49.3|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|49.0|49.6||
Demographics|Sex (Percent)|2014|Male|All|49.5|Portland (Multnomah County), OR|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Sex (Percent)|2014|Male|All|49.6|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|48.5|50.7||
Demographics|Sex (Percent)|2014|Male|All|49.7|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|49.4|50.0||
Demographics|Sex (Percent)|2014|Male|All|49.7|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|49.3|50.1||
Demographics|Sex (Percent)|2014|Male|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.|49.9|50.1||
Demographics|Sex (Percent)|2014|Male|All|50.0|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|49.6|50.4||
Demographics|Sex (Percent)|2014|Male|All|50.1|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|49.7|50.5||
Demographics|Sex (Percent)|2014|Male|All|50.1|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|50.0|50.2||
Demographics|Sex (Percent)|2014|Male|All|50.3|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.|50.2|50.4||
Demographics|Sex (Percent)|2014|Male|All|50.4|Austin, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2014|Male|All|50.6|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|49.8|51.4||
Demographics|Sex (Percent)|2014|Male|All|50.7|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|49.9|51.5||
Demographics|Sex (Percent)|2014|Male|All|50.9|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.|50.8|51.0||
Demographics|Sex (Percent)|2015|Female|All|49.2|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Sex (Percent)|2015|Female|All|49.5|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Sex (Percent)|2015|Female|All|49.5|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Sex (Percent)|2015|Female|All|49.6|Austin, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Female|All|49.7|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Sex (Percent)|2015|Female|All|49.7|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Sex (Percent)|2015|Female|All|49.8|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Sex (Percent)|2015|Female|All|49.8|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Sex (Percent)|2015|Female|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Sex (Percent)|2015|Female|All|50.0|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Sex (Percent)|2015|Female|All|50.1|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Sex (Percent)|2015|Female|All|50.5|Portland (Multnomah County), OR|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Sex (Percent)|2015|Female|All|50.7|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Sex (Percent)|2015|Female|All|50.8|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Sex (Percent)|2015|Female|All|50.8|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Female|All|50.9|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Sex (Percent)|2015|Female|All|51.1|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Sex (Percent)|2015|Female|All|51.2|Columbus, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Female|All|51.4|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Sex (Percent)|2015|Female|All|51.5|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Sex (Percent)|2015|Female|All|51.5|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Sex (Percent)|2015|Female|All|51.8|Indianapolis (Marion County), IN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Female|All|51.9|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Sex (Percent)|2015|Female|All|51.9|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Sex (Percent)|2015|Female|All|52.0|Charlotte, NC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Female|All|52.0|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Sex (Percent)|2015|Female|All|52.3|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Sex (Percent)|2015|Female|All|52.4|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Sex (Percent)|2015|Female|All|52.7|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Sex (Percent)|2015|Female|All|52.7|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Sex (Percent)|2015|Female|All|52.8|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Sex (Percent)|2015|Male|All|47.2|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Sex (Percent)|2015|Male|All|47.3|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Sex (Percent)|2015|Male|All|47.3|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Sex (Percent)|2015|Male|All|47.6|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Sex (Percent)|2015|Male|All|47.7|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Sex (Percent)|2015|Male|All|48.0|Charlotte, NC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Male|All|48.0|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Sex (Percent)|2015|Male|All|48.1|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Sex (Percent)|2015|Male|All|48.1|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Sex (Percent)|2015|Male|All|48.2|Indianapolis (Marion County), IN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Male|All|48.5|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Sex (Percent)|2015|Male|All|48.5|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Sex (Percent)|2015|Male|All|48.6|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Sex (Percent)|2015|Male|All|48.8|Columbus, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Male|All|48.9|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Sex (Percent)|2015|Male|All|49.1|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Sex (Percent)|2015|Male|All|49.2|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Sex (Percent)|2015|Male|All|49.2|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Male|All|49.3|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Sex (Percent)|2015|Male|All|49.5|Portland (Multnomah County), OR|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Sex (Percent)|2015|Male|All|49.9|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Sex (Percent)|2015|Male|All|50.0|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Sex (Percent)|2015|Male|All|50.0|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Sex (Percent)|2015|Male|All|50.2|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Sex (Percent)|2015|Male|All|50.2|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Sex (Percent)|2015|Male|All|50.3|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Sex (Percent)|2015|Male|All|50.3|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Sex (Percent)|2015|Male|All|50.4|Austin, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2015|Male|All|50.5|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Sex (Percent)|2015|Male|All|50.5|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Sex (Percent)|2015|Male|All|50.8|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Sex (Percent)|2016|Female|All|48.4|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Sex (Percent)|2016|Female|All|49.0|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Sex (Percent)|2016|Female|All|49.2|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Sex (Percent)|2016|Female|All|49.6|Austin, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Female|All|49.7|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Sex (Percent)|2016|Female|All|49.7|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Sex (Percent)|2016|Female|All|49.9|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Sex (Percent)|2016|Female|All|50.0|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Sex (Percent)|2016|Female|All|50.1|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Sex (Percent)|2016|Female|All|50.4|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Sex (Percent)|2016|Female|All|50.4|Portland (Multnomah County), OR|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Sex (Percent)|2016|Female|All|50.5|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Sex (Percent)|2016|Female|All|50.7|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Sex (Percent)|2016|Female|All|50.7|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Sex (Percent)|2016|Female|All|50.8|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Sex (Percent)|2016|Female|All|50.8|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Female|All|50.9|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Sex (Percent)|2016|Female|All|50.9|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Sex (Percent)|2016|Female|All|51.1|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Sex (Percent)|2016|Female|All|51.2|Columbus, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Female|All|51.2|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Sex (Percent)|2016|Female|All|51.5|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Sex (Percent)|2016|Female|All|51.5|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Sex (Percent)|2016|Female|All|51.8|Indianapolis (Marion County), IN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Female|All|52.0|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Sex (Percent)|2016|Female|All|52.0|Charlotte, NC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Female|All|52.3|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Sex (Percent)|2016|Female|All|52.3|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Sex (Percent)|2016|Female|All|52.5|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Sex (Percent)|2016|Female|All|52.6|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Sex (Percent)|2016|Female|All|53.0|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Sex (Percent)|2016|Male|All|47.0|Baltimore, MD|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Sex (Percent)|2016|Male|All|47.4|Philadelphia, PA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Sex (Percent)|2016|Male|All|47.5|Washington, DC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Sex (Percent)|2016|Male|All|47.7|Detroit, MI|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Sex (Percent)|2016|Male|All|47.7|New York City, NY|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Sex (Percent)|2016|Male|All|48.0|Boston, MA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Sex (Percent)|2016|Male|All|48.0|Charlotte, NC|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Male|All|48.2|Indianapolis (Marion County), IN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Male|All|48.5|Chicago, Il|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Sex (Percent)|2016|Male|All|48.5|Miami (Miami-Dade County), FL|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Sex (Percent)|2016|Male|All|48.8|Columbus, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Male|All|48.8|Kansas City, MO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Sex (Percent)|2016|Male|All|48.9|Fort Worth (Tarrant County), TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Sex (Percent)|2016|Male|All|49.1|Cleveland, OH|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Sex (Percent)|2016|Male|All|49.1|Long Beach, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Sex (Percent)|2016|Male|All|49.2|Dallas, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Sex (Percent)|2016|Male|All|49.2|U.S. Total, U.S. Total|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Male|All|49.3|Los Angeles, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Sex (Percent)|2016|Male|All|49.3|Oakland (Alameda County), CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Sex (Percent)|2016|Male|All|49.5|San Antonio, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Sex (Percent)|2016|Male|All|49.6|Phoenix, AZ|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Sex (Percent)|2016|Male|All|49.6|Portland (Multnomah County), OR|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Sex (Percent)|2016|Male|All|49.9|Las Vegas (Clark County), NV|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Sex (Percent)|2016|Male|All|50.0|Houston, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Sex (Percent)|2016|Male|All|50.1|Denver, CO|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Sex (Percent)|2016|Male|All|50.3|San Diego County, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Sex (Percent)|2016|Male|All|50.3|Seattle, WA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Sex (Percent)|2016|Male|All|50.4|Austin, TX|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Sex (Percent)|2016|Male|All|50.8|San Jose, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Sex (Percent)|2016|Male|All|51.0|San Francisco, CA|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Sex (Percent)|2016|Male|All|51.6|Minneapolis, MN|Percentage distribution of the population by sex using US Census Bureau, American Community Survey 1-year estimates, or something similar|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Total Population (People)|2012|Both|All|390923.0|Cleveland, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|390876.0|390970.0||
Demographics|Total Population (People)|2012|Both|All|392871.0|Minneapolis, MN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|392823.0|392919.0||
Demographics|Total Population (People)|2012|Both|All|400740.0|Oakland (Alameda County), CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|400689.0|400791.0||
Demographics|Total Population (People)|2012|Both|All|464346.0|Kansas City, MO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|464129.0|464563.0||
Demographics|Total Population (People)|2012|Both|All|467888.0|Long Beach, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|467843.0|467933.0||
Demographics|Total Population (People)|2012|Both|All|621342.0|Baltimore, MD|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Total Population (People)|2012|Both|All|632323.0|Washington, DC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Total Population (People)|2012|Both|All|634265.0|Denver, CO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Total Population (People)|2012|Both|All|634541.0|Seattle, WA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|634483.0|634599.0||
Demographics|Total Population (People)|2012|Both|All|637516.0|Boston, MA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|635212.0|639820.0||
Demographics|Total Population (People)|2012|Both|All|701524.0|Detroit, MI|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|701456.0|701592.0||
Demographics|Total Population (People)|2012|Both|All|825863.0|San Francisco, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Total Population (People)|2012|Both|All|982783.0|San Jose, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|982702.0|982864.0||
Demographics|Total Population (People)|2012|Both|All|1383194.0|San Antonio, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|1382754.0|1383634.0||
Demographics|Total Population (People)|2012|Both|All|1488759.0|Phoenix, AZ|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1488669.0|1488849.0||
Demographics|Total Population (People)|2012|Both|All|1547607.0|Philadelphia, PA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Total Population (People)|2012|Both|All|1880153.0|Fort Worth (Tarrant County), TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Total Population (People)|2012|Both|All|2000759.0|Las Vegas (Clark County), NV|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Total Population (People)|2012|Both|All|2161686.0|Houston, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|2159401.0|2163971.0||
Demographics|Total Population (People)|2012|Both|All|2453843.0|Dallas, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Total Population (People)|2012|Both|All|2591035.0|Miami (Miami-Dade County), FL|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Total Population (People)|2012|Both|All|2714844.0|Chicago, Il|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|2714794.0|2714894.0||
Demographics|Total Population (People)|2012|Both|All|3177063.0|San Diego County, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Total Population (People)|2012|Both|All|3857786.0|Los Angeles, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|3857730.0|3857842.0||
Demographics|Total Population (People)|2012|Both|All|8336697.0|New York City, NY|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Total Population (People)|2012|Both|All|313914040.0|U.S. Total, U.S. Total|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2013|Both|All|390106.0|Cleveland, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|390064.0|390148.0||
Demographics|Total Population (People)|2013|Both|All|400079.0|Minneapolis, MN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|400029.0|400129.0||
Demographics|Total Population (People)|2013|Both|All|406228.0|Oakland (Alameda County), CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|406125.0|406331.0||
Demographics|Total Population (People)|2013|Both|All|467082.0|Kansas City, MO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|466866.0|467298.0||
Demographics|Total Population (People)|2013|Both|All|469384.0|Long Beach, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|469241.0|469527.0||
Demographics|Total Population (People)|2013|Both|All|622104.0|Baltimore, MD|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Total Population (People)|2013|Both|All|644710.0|Boston, MA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|640938.0|648482.0||
Demographics|Total Population (People)|2013|Both|All|646449.0|Washington, DC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Total Population (People)|2013|Both|All|649495.0|Denver, CO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Total Population (People)|2013|Both|All|652429.0|Seattle, WA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|652332.0|652526.0||
Demographics|Total Population (People)|2013|Both|All|688740.0|Detroit, MI|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|688670.0|688810.0||
Demographics|Total Population (People)|2013|Both|All|837442.0|San Francisco, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Total Population (People)|2013|Both|All|998514.0|San Jose, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|998406.0|998622.0||
Demographics|Total Population (People)|2013|Both|All|1409000.0|San Antonio, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|1408913.0|1409087.0||
Demographics|Total Population (People)|2013|Both|All|1513350.0|Phoenix, AZ|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1513249.0|1513451.0||
Demographics|Total Population (People)|2013|Both|All|1553165.0|Philadelphia, PA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Total Population (People)|2013|Both|All|1911541.0|Fort Worth (Tarrant County), TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Total Population (People)|2013|Both|All|2027868.0|Las Vegas (Clark County), NV|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Total Population (People)|2013|Both|All|2197374.0|Houston, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|2194509.0|2200239.0||
Demographics|Total Population (People)|2013|Both|All|2480331.0|Dallas, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Total Population (People)|2013|Both|All|2617176.0|Miami (Miami-Dade County), FL|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Total Population (People)|2013|Both|All|2718789.0|Chicago, Il|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|2718737.0|2718841.0||
Demographics|Total Population (People)|2013|Both|All|3211252.0|San Diego County, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Total Population (People)|2013|Both|All|3884340.0|Los Angeles, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|3884198.0|3884482.0||
Demographics|Total Population (People)|2013|Both|All|8405837.0|New York City, NY|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Total Population (People)|2013|Both|All|316128839.0|U.S. Total, U.S. Total|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2014|Both|All|389524.0|Cleveland, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.|389486.0|389562.0||
Demographics|Total Population (People)|2014|Both|All|407181.0|Minneapolis, MN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|407086.0|407276.0||
Demographics|Total Population (People)|2014|Both|All|413782.0|Oakland (Alameda County), CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.|413723.0|413841.0||
Demographics|Total Population (People)|2014|Both|All|470816.0|Kansas City, MO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.|470616.0|471016.0||
Demographics|Total Population (People)|2014|Both|All|473605.0|Long Beach, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.|473496.0|473714.0||
Demographics|Total Population (People)|2014|Both|All|622793.0|Baltimore, MD|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Total Population (People)|2014|Both|All|656051.0|Boston, MA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.|653176.0|658926.0||
Demographics|Total Population (People)|2014|Both|All|658893.0|Washington, DC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Total Population (People)|2014|Both|All|663862.0|Denver, CO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Total Population (People)|2014|Both|All|668337.0|Seattle, WA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.|668287.0|668387.0||
Demographics|Total Population (People)|2014|Both|All|680281.0|Detroit, MI|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.|680210.0|680352.0||
Demographics|Total Population (People)|2014|Both|All|776712.0|Portland (Multnomah County), OR|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Total Population (People)|2014|Both|All|852469.0|San Francisco, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Total Population (People)|2014|Both|All|934243.0|Indianapolis (Marion County), IN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2014|Both|All|1012539.0|Charlotte, NC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2014|Both|All|1015796.0|San Jose, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.|1015726.0|1015866.0||
Demographics|Total Population (People)|2014|Both|All|1151145.0|Austin, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2014|Both|All|1231393.0|Columbus, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2014|Both|All|1436723.0|San Antonio, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.|1436539.0|1436907.0||
Demographics|Total Population (People)|2014|Both|All|1537045.0|Phoenix, AZ|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|1536946.0|1537144.0||
Demographics|Total Population (People)|2014|Both|All|1560297.0|Philadelphia, PA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Total Population (People)|2014|Both|All|1945360.0|Fort Worth (Tarrant County), TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Total Population (People)|2014|Both|All|2069681.0|Las Vegas (Clark County), NV|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Total Population (People)|2014|Both|All|2240796.0|Houston, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.|2238157.0|2243435.0||
Demographics|Total Population (People)|2014|Both|All|2518638.0|Dallas, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Total Population (People)|2014|Both|All|2662874.0|Miami (Miami-Dade County), FL|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Total Population (People)|2014|Both|All|2722407.0|Chicago, Il|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|2722328.0|2722486.0||
Demographics|Total Population (People)|2014|Both|All|3263431.0|San Diego County, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Total Population (People)|2014|Both|All|3928827.0|Los Angeles, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|3928733.0|3928921.0||
Demographics|Total Population (People)|2014|Both|All|8491079.0|New York City, NY|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Total Population (People)|2014|Both|All|318857056.0|U.S. Total, U.S. Total|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2015|Both|All|388059.0|Cleveland, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Total Population (People)|2015|Both|All|410935.0|Minneapolis, MN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Total Population (People)|2015|Both|All|419278.0|Oakland (Alameda County), CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Total Population (People)|2015|Both|All|474172.0|Long Beach, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Total Population (People)|2015|Both|All|475361.0|Kansas City, MO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Total Population (People)|2015|Both|All|621849.0|Baltimore, MD|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Total Population (People)|2015|Both|All|669469.0|Boston, MA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Total Population (People)|2015|Both|All|672228.0|Washington, DC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Total Population (People)|2015|Both|All|677124.0|Detroit, MI|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Total Population (People)|2015|Both|All|682545.0|Denver, CO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Total Population (People)|2015|Both|All|684443.0|Seattle, WA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Total Population (People)|2015|Both|All|790294.0|Portland (Multnomah County), OR|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Total Population (People)|2015|Both|All|864816.0|San Francisco, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Total Population (People)|2015|Both|All|939020.0|Indianapolis (Marion County), IN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2015|Both|All|1026919.0|San Jose, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Total Population (People)|2015|Both|All|1034070.0|Charlotte, NC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2015|Both|All|1176558.0|Austin, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2015|Both|All|1251722.0|Columbus, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2015|Both|All|1469824.0|San Antonio, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Total Population (People)|2015|Both|All|1563001.0|Phoenix, AZ|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Total Population (People)|2015|Both|All|1567442.0|Philadelphia, PA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Total Population (People)|2015|Both|All|1982498.0|Fort Worth (Tarrant County), TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Total Population (People)|2015|Both|All|2114801.0|Las Vegas (Clark County), NV|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Total Population (People)|2015|Both|All|2298628.0|Houston, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Total Population (People)|2015|Both|All|2553385.0|Dallas, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Total Population (People)|2015|Both|All|2693117.0|Miami (Miami-Dade County), FL|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Total Population (People)|2015|Both|All|2720556.0|Chicago, Il|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Total Population (People)|2015|Both|All|3299521.0|San Diego County, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Total Population (People)|2015|Both|All|3971896.0|Los Angeles, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Total Population (People)|2015|Both|All|8550405.0|New York City, NY|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Total Population (People)|2015|Both|All|321418821.0|U.S. Total, U.S. Total|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2016|Both|All|385810.0|Cleveland, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Demographics|Total Population (People)|2016|Both|All|413645.0|Minneapolis, MN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Demographics|Total Population (People)|2016|Both|All|419987.0|Oakland (Alameda County), CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Demographics|Total Population (People)|2016|Both|All|470140.0|Long Beach, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Demographics|Total Population (People)|2016|Both|All|481360.0|Kansas City, MO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Demographics|Total Population (People)|2016|Both|All|614664.0|Baltimore, MD|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Demographics|Total Population (People)|2016|Both|All|672829.0|Detroit, MI|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Demographics|Total Population (People)|2016|Both|All|672840.0|Boston, MA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Demographics|Total Population (People)|2016|Both|All|681170.0|Washington, DC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Demographics|Total Population (People)|2016|Both|All|693060.0|Denver, CO|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Demographics|Total Population (People)|2016|Both|All|704358.0|Seattle, WA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Demographics|Total Population (People)|2016|Both|All|799766.0|Portland (Multnomah County), OR|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Demographics|Total Population (People)|2016|Both|All|870887.0|San Francisco, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Demographics|Total Population (People)|2016|Both|All|941229.0|Indianapolis (Marion County), IN|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2016|Both|All|1025373.0|San Jose, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Demographics|Total Population (People)|2016|Both|All|1054835.0|Charlotte, NC|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2016|Both|All|1199323.0|Austin, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2016|Both|All|1264518.0|Columbus, OH|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Demographics|Total Population (People)|2016|Both|All|1492494.0|San Antonio, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Demographics|Total Population (People)|2016|Both|All|1567872.0|Philadelphia, PA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Demographics|Total Population (People)|2016|Both|All|1615041.0|Phoenix, AZ|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Demographics|Total Population (People)|2016|Both|All|2016872.0|Fort Worth (Tarrant County), TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Demographics|Total Population (People)|2016|Both|All|2155664.0|Las Vegas (Clark County), NV|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Demographics|Total Population (People)|2016|Both|All|2304388.0|Houston, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Demographics|Total Population (People)|2016|Both|All|2574984.0|Dallas, TX|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Demographics|Total Population (People)|2016|Both|All|2704965.0|Chicago, Il|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Demographics|Total Population (People)|2016|Both|All|2712945.0|Miami (Miami-Dade County), FL|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Demographics|Total Population (People)|2016|Both|All|3317749.0|San Diego County, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Demographics|Total Population (People)|2016|Both|All|3976324.0|Los Angeles, CA|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Demographics|Total Population (People)|2016|Both|All|8537673.0|New York City, NY|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Demographics|Total Population (People)|2016|Both|All|323127515.0|U.S. Total, U.S. Total|Total population using US Census Bureau, American Community Survey 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP05 - Demographic and Housing Estimates||||||
Environment|Bike Score|2018|Both|All|40.0|Kansas City, MO|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|42.0|San Antonio, TX|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|44.0|Dallas, TX|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|47.0|Columbus, OH|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|49.0|Houston, TX|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|51.0|Cleveland, OH|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|54.0|Phoenix, AZ|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|55.0|Detroit, MI|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|56.0|Baltimore, MD|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|56.0|Los Angeles, CA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|57.0|San Jose, CA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|63.0|Seattle, WA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|65.0|New York City, NY|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|66.0|Long Beach, CA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|68.0|Philadelphia, PA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|69.0|Washington, DC|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|70.0|Boston, MA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|70.0|Chicago, Il|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|71.0|Denver, CO|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|75.0|San Francisco, CA|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Bike Score|2018|Both|All|81.0|Minneapolis, MN|Walk Score calculates Bike Score as a measure of whether a city is good for biking on a scale from 0 - 100 based on four equally weighted components: Bike lanes, Hills, Destinations and road connectivity, and Bike commuting mode share. A score of 100 indicates a complete Biker's Paradise (from WalkScore.com).|Walk Score||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|0.1|Charlotte, NC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|1.1|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|1.8|Oakland (Alameda County), CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Lead Poisoning Prevention Branch; http://www.cdph.ca.gov/programs/CLPPB/Pages/default.aspx ||Data for county level. Alameda County (excluding Berkeley)||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|3.8|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|4.0|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|4.2|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|4.3|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|6.0|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|All|10.7|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|American Indian/Alaska Native|6.7|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Asian/PI|6.1|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Asian/PI|7.9|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Black|1.2|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Black|5.1|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Hispanic|3.4|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Hispanic|4.8|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Multiracial|36.4|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Other|0.8|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year. Race/ethinicty of Other include all except white, black, hispanic, asian/PI. It includes those without race/ethnicity information.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|Other|3.1|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|White|2.1|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Both|White|6.1|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Female|All|1.1|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Female|All|3.6|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Female|All|3.7|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Female|All|3.9|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Female|All|4.0|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Female|All|10.0|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Male|All|1.1|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Male|All|3.8|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Male|All|4.1|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Male|All|4.6|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Male|All|4.7|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2010|Male|All|11.4|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|0.0|Charlotte, NC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|1.0|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|2.1|Oakland (Alameda County), CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Lead Poisoning Prevention Branch; http://www.cdph.ca.gov/programs/CLPPB/Pages/default.aspx ||Data for county level. Alameda County (excluding Berkeley)||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|2.9|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|3.3|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|3.5|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|3.8|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|4.2|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|4.3|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|All|7.7|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|American Indian/Alaska Native|2.3|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Asian/PI|5.8|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Asian/PI|7.0|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Black|1.0|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Black|3.0|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Hispanic|0.9|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Hispanic|4.1|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Multiracial|25.0|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Other|0.9|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year. Race/ethinicty of Other include all except white, black, hispanic, asian/PI. It includes those without race/ethnicity information.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|Other|2.8|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|White|0.8|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Both|White|5.3|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|1.0|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|2.6|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|3.0|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|3.2|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|3.4|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|4.1|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Female|All|7.3|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|1.0|Houston, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Houston Health Department|Numerator = # of children (0-72 months) with blood lead level at or over 5 ug/dl; Denominator = # of children (0-72 months) screened|Screened for the first time with venous blood test in that year||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|3.0|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|3.2|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|3.9|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|4.1|San Antonio, TX|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Texas Childhood Lead Poisoning Prevention Program (TCLPPP)|Unduplicated children under age 6 years at date of test. Children may be counted in multiple years. Cells with 1_4 children shown as < 5 to protect identity. %  5 mcg/dL is percent among children tested in respective category.|Data includes Bexar County, TX, not just San Antonio||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|4.4|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2011|Male|All|8.1|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|0.0|Charlotte, NC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|0.6|Portland (Multnomah County), OR|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|ORPHEUS for numerator and State's report to CDC for demoninator|||||0.4|0.9
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|1.5|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Represents confirmed prevalent cases per CDC case definition (one venous specimen  5 /dL, or two capillary specimens  5 /dL within 12 weeks of each other).||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|1.6|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|2.1|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|2.4|Oakland (Alameda County), CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch|elevated lead poisoning defined by CA as >4.5 ug/dl|Alameda County data, not just Oakland||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|2.5|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|2.5|New York City, NY|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|New York City Department of Health and Mental Hygiene, Healthy Homes Program|Numerator: Number of NYC children under 6 years of age with blood lead levels at or above 5mcg/dL in 2012. Denominator: Number of NYC children under 6 years of age screened in 2012.|||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|2.7|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|3.2|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|3.3|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|3.7|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|4.8|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.4|5.2
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|5.5|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|7.6|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|15.6|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|All|17.4|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|Black|4.3|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||3.7|5.0
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|Black|18.2|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|Hispanic|3.4|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||2.7|4.2
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|Other|4.5|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||3.6|5.5
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|White|5.6|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Both|White|6.7|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||5.8|7.7
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|1.3|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Gender data are missing for fewer than 10 children tested in 2012.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|1.8|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|2.0|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|2.2|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|2.4|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|3.1|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|3.3|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|4.6|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.0|5.2
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|5.2|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|7.2|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Female|All|13.0|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|1.4|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|1.6|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Gender data are missing for fewer than 10 children tested in 2012.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|2.1|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|2.6|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|3.1|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|3.6|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|4.0|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|5.0|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.4|5.6
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|5.9|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|8.0|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2012|Male|All|21.5|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|0.3|Charlotte, NC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|0.9|Portland (Multnomah County), OR|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|ORPHEUS for numerator and State's report to CDC for demoninator|||||0.7|1.3
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|1.1|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|1.1|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Represents confirmed prevalent cases per CDC case definition (one venous specimen  5 /dL, or two capillary specimens  5 /dL within 12 weeks of each other).||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|1.6|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|1.7|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|2.1|Oakland (Alameda County), CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Lead Poisoning Prevention Branch http://www.cdph.ca.gov/programs/CLPPB/Pages/default.aspx ||Data is for Alameda County. ||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|2.2|New York City, NY|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|New York City Department of Health and Mental Hygiene, Healthy Homes Program|Numerator: Number of NYC children under 6 years of age with blood lead levels at or above 5mcg/dL in 2013. Denominator: Number of NYC children under 6 years of age screened in 2013.|||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|2.3|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|2.8|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|2.8|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|3.1|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|4.4|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|4.4|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.0|4.8
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|7.1|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|8.9|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|All|13.9|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Asian/PI|0.0|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Black|3.4|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||2.9|4.1
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Black|6.8|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Black|11.0|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Hispanic|0.9|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Hispanic|3.3|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||2.5|4.3
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Hispanic|4.3|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Other|0.2|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|Other|4.2|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||3.4|5.2
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|White|0.0|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|White|1.5|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Both|White|6.3|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||5.4|7.3
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|1.1|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|1.1|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Gender data are missing for fewer than 10 children tested in 2013.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|1.6|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|2.1|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|2.5|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|2.7|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|3.0|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|4.0|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||3.5|4.5
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|4.1|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|6.4|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|6.4|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Female|All|7.2|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|1.0|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|1.1|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Gender data are missing for fewer than 10 children tested in 2013.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|1.8|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|2.3|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|3.0|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|3.2|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|3.3|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|4.8|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|5.0|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.4|5.6
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|7.5|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|7.7|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2013|Male|All|10.5|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|0.3|Charlotte, NC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|0.7|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|1.1|Portland (Multnomah County), OR|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|ORPHEUS for numerator and State's report to CDC for demoninator|||||0.7|1.2
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|1.4|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|1.5|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|1.5|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Represents confirmed prevalent cases per CDC case definition (one venous specimen  5 /dL, or two capillary specimens  5 /dL within 12 weeks of each other).||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|1.6|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|2.1|New York City, NY|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|New York City Department of Health and Mental Hygiene, Healthy Homes Program|Numerator: Number of NYC children under 6 years of age with blood lead levels at or above 5mcg/dL in 2014. Denominator: Number of NYC children under 6 years of age screened in 2014.|||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|2.1|Oakland (Alameda County), CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Lead Poisoning Prevention Branch http://www.cdph.ca.gov/programs/CLPPB/Pages/default.aspx ||Data is for Alameda County. ||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|2.4|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|2.4|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|2.9|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|4.0|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|4.6|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.1|5.1
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|5.5|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|6.9|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|All|14.2|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Asian/PI|0.0|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Black|3.0|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||2.4|3.8
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Black|7.0|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Black|7.8|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Hispanic|0.5|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Hispanic|1.2|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Hispanic||Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Other|6.1|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.9|7.4
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|Other||Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|White|0.0|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|White|0.9|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Both|White|5.8|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.8|6.8
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|0.4|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|1.4|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Gender data are missing for fewer than 10 children tested in 2014.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|1.5|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|1.5|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|2.3|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|2.3|Los Angeles, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Annual data CD released by California Childhood Lead Poisoning Prevention Branch for the calendar year 2012.|For the data in the CD, the following steps were conducted using SAS software: 1) 4 quarters of data were sorted out and joined by patient ID; 2) the joined data were then sorted out by patient ID, blood lead test result and blood drawn date; 3) only the highest blood lead level was kept for each individual; 4) frequencies were generated for all children under 6 years old; and 5) frequencies were generated for all children under 6 years with blood lead levels at or greater than 4.5 mcg/dL.|County level data only. Unprocessed records were not included in analysis.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|2.8|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|3.8|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|4.3|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||3.7|5.0
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|6.0|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|6.4|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Female|All|6.4|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|1.1|Las Vegas (Clark County), NV|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|1.5|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|1.6|Washington, DC|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Healthy Homes Lead Poisoning Surveillance System, Department of Energy & Environment|Venous or capillary blood sampling|Gender data are missing for fewer than 10 children tested in 2014.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|1.7|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|2.6|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|3.0|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|4.4|Chicago, Il|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Chicago Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|4.8|Indianapolis (Marion County), IN|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|MCPHD Lead Screening Data|||||4.2|5.5
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|5.1|Miami (Miami-Dade County), FL|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|7.4|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2014|Male|All|7.7|Cleveland, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Data analyzed by Epidemiology, Surveillance and Informatics at the Cuyahoga County Board of Health.  Original data obtained through the Ohio Department of Health's Ohio Healthy Homes Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|0.9|Portland (Multnomah County), OR|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|ORPHEUS for numerator and State's report to CDC for demoninator|||||0.7|1.2
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|1.3|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|1.4|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|2.3|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|2.4|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|3.2|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Both|All|6.1|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Female|All|1.2|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Female|All|2.2|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Female|All|2.6|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Female|All|3.0|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Female|All|6.1|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Male|All|1.4|San Diego County, CA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|California Department of Public Health Childhood Lead Poisoning Prevention Branch, RASSCLE surveillance database|If a child had more than one test during a year, the highest result based on a venous sample was used; if no venous result, the highest result based on a capillary/unknown sample was used.|Values of 4.5-4.9 were rounded up and included. Analysis completed by County of San Diego Childhood Lead Poisoning Prevention Program.||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Male|All|2.4|Boston, MA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Childhood Lead Poisoning Prevention Program, Massachusetts Department of Public Health||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Male|All|2.4|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Male|All|3.4|Kansas City, MO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2015|Male|All|6.2|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Both|All|0.7|Portland (Multnomah County), OR|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.||||||0.5|0.9
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Both|All|1.3|Columbus, OH|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Ohio Healthy Homes and Lead Poisoning Prevention Program||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Both|All|1.7|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Both|All|6.0|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Female|All|2.0|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Female|All|5.7|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Male|All|1.5|Denver, CO|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children with elevated blood levels; Denominator = # of children under 6 screened.|Colorado Environmental Public Health Tracking  -  request for reported lead tests by race, ethnicity and gender.||||||
Environment|Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels|2016|Male|All|6.4|Philadelphia, PA|Percent of children under 6 that have blood levels at/over 5 ug/dl. Numerator = # of children under 6 with newly identified elevated blood levels at/over 5ug/dl; Denominator = # of children under 6 screened.|PA- NEDSS||||||
Environment|Transit Score|2018|Both|All|29.0|Kansas City, MO|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|31.0|Columbus, OH|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|36.0|Phoenix, AZ|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|36.0|San Antonio, TX|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|37.0|Houston, TX|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|38.0|Detroit, MI|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|40.0|Dallas, TX|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|41.0|San Jose, CA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|47.0|Cleveland, OH|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|48.0|Denver, CO|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|51.0|Long Beach, CA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|51.0|Los Angeles, CA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|57.0|Baltimore, MD|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|58.0|Minneapolis, MN|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|60.0|Seattle, WA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|65.0|Chicago, Il|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|67.0|Philadelphia, PA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|68.0|Washington, DC|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|73.0|Boston, MA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|80.0|San Francisco, CA|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Transit Score|2018|Both|All|85.0|New York City, NY|Walk Score calculates transit score, a patented measure of how well a city is served by public transit on a scale from 0 to 100, based on features of nearby transit routes (nearest stops on the route, frequency of the routes, and types of routes). A score of 100 indicates a complete rider's paradise, with world class transportation (from WalkScore.com).|Walk Score||||||
Environment|Walkability|2018|Both|All|34.0|Kansas City, MO|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|38.0|San Antonio, TX|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|41.0|Columbus, OH|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|41.0|Phoenix, AZ|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|46.0|Dallas, TX|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|51.0|San Jose, CA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|60.0|Cleveland, OH|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|61.0|Denver, CO|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|67.0|Los Angeles, CA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|69.0|Baltimore, MD|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|69.0|Minneapolis, MN|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|70.0|Long Beach, CA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|73.0|Seattle, WA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|77.0|Washington, DC|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|78.0|Chicago, Il|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|79.0|Philadelphia, PA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|81.0|Boston, MA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|86.0|San Francisco, CA|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Environment|Walkability|2018|Both|All|89.0|New York City, NY|The walkability of a city using a system patented by Walk Score, assessing hundreds of walking routes and distance to nearby amenities, of various kinds, to give a score from 0 to 100 (0 indicating a city is entirely car-dependent, 100 indicating a perfect walker's paradise - from Walk Score).|Walk Score||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|7.9|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|8.8|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.4|11.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|9.5|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|11.8|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||9.1|14.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|12.9|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|15.3|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||13.5|17.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|15.6|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|15.7|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|16.9|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|18.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||16.0|20.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|19.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|20.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|21.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|All|24.9|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|American Indian/Alaska Native|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|0.0|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|0.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|5.6|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|9.4|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|10.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|11.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||4.4|18.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|14.7|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|18.9|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.8|23.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI|43.6|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|4.1|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|5.8|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|8.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|8.9|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|9.9|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|10.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|11.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||7.1|17.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|12.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||2.6|22.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|13.1|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|15.1|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black|28.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Black||San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|0.8|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|2.5|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|5.8|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|7.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|8.4|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|11.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|13.6|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||11.4|15.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|14.9|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||3.0|26.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|16.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|17.9|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||13.6|23.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|20.9|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic|23.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other|0.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other|5.7|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other|15.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||1.9|29.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other||Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|Other||San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|4.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|5.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|5.4|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|6.1|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||3.5|8.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|11.2|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||7.9|14.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|11.5|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|12.8|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|14.8|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||11.5|18.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|16.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|19.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|20.4|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||16.6|24.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|20.7|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Both|White|29.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|7.7|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|9.3|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.9|12.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|9.6|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|10.8|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||8.8|12.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|11.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|12.2|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||8.2|16.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|14.5|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|15.0|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|16.4|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||13.7|19.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|18.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|18.9|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|19.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||16.1|22.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|21.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|21.8|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Female|All|23.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|6.0|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|7.7|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|8.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|8.3|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.0|11.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|10.0|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||8.0|12.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|11.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|11.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||7.7|15.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|14.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||11.7|16.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|15.5|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|16.3|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|17.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.1|20.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|19.4|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|19.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|19.8|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2010|Male|All|28.4|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|6.1|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.0|7.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|6.7|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|7.8|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.6|10.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|8.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|9.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||7.1|12.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|11.1|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|11.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|11.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|13.6|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|14.2|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|15.3|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|16.7|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.6|18.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|17.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|18.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|18.4|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|20.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|All|24.0|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|0.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|6.2|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|8.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|10.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||3.6|17.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI|23.3|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||18.8|28.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Asian/PI||Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|3.7|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|4.5|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|5.1|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|5.4|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|6.7|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|8.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|8.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|11.0|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.3|15.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|11.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||7.1|17.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|13.7|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|14.2|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black|14.4|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Black||Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|1.1|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|1.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|7.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|7.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|8.5|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|10.9|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|12.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|12.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|15.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|18.8|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.4|24.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|26.9|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic|27.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Hispanic||Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Other|0.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Other|6.7|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Other|18.8|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||3.8|33.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Other|28.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|1.1|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|4.4|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|6.1|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|6.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|8.7|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|8.9|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||6.0|11.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|9.4|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|9.5|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|10.8|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||8.0|14.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|11.8|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|14.8|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|22.6|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White|23.8|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|6.1|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||4.5|7.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|7.2|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||4.2|10.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|8.1|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|8.6|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|10.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|10.5|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|11.2|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||7.4|14.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|11.7|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|13.0|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|15.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|15.7|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|16.1|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|16.4|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|17.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.2|20.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|19.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|19.2|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Female|All|24.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|5.3|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|6.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|6.2|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||4.7|7.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|7.9|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|7.9|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||4.7|11.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|8.5|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.2|11.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|8.5|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|11.6|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|11.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|12.7|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|14.5|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|16.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||13.3|19.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|17.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|20.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|21.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2011|Male|All|23.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|6.4|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||4.9|8.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|7.1|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.9|8.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|7.2|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|7.6|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.4|9.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|8.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|8.6|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|9.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|11.1|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|11.5|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|12.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|12.3|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|12.8|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|12.8|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|14.0|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|14.9|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||12.9|17.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|15.5|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|15.7|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|16.4|U.S. Total, U.S. Total|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Foodborne Diseases Active Surveillance Network (FoodNet) Table 2b Incidence of culture-confirmed bacterial and laboratory-confirmed parasitic infections, and postdiarrheal hemolytic uremic syndrome (HUS), by year and pathogen, Foodborne Diseases Active Surveillance Network (FoodNet), United States, 1996_2014*||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|16.5|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|16.7|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|18.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|18.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|23.7|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|All|31.6|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|American Indian/Alaska Native|0.0|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|American Indian/Alaska Native|28.4|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|American Indian/Alaska Native|40.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|American Indian/Alaska Native|74.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|0.7|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|4.9|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|5.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|6.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|7.9|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|7.9|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|10.2|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|12.4|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|13.4|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI|17.4|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||13.5|22.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|0.8|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|3.7|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|6.0|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|6.7|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|7.7|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|8.3|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|8.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|8.9|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|9.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|9.9|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|10.8|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|11.8|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|12.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||7.5|18.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|16.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|18.2|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|21.1|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black|34.3|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|0.5|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|2.5|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|5.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|6.8|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|8.0|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|8.1|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|11.0|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|12.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|12.9|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|14.4|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||10.6|19.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|14.9|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|15.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|15.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|24.8|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic|25.8|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Multiracial|7.7|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other|3.3|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other|5.7|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other|8.3|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other|61.6|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other|248.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|Other||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|2.1|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|4.1|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|4.1|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|4.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|5.5|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|6.0|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||3.4|8.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|8.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|8.8|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|9.2|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|9.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|10.3|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|10.3|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||7.6|13.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|10.7|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|12.5|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|16.5|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|20.2|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|21.4|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|24.0|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White|25.9|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Both|White||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|5.6|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|5.7|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|6.1|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|6.8|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||3.9|9.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|7.1|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|7.5|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.8|9.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|8.7|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|9.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|9.8|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|12.1|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|12.6|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|12.7|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|13.0|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|13.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|13.5|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|13.9|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|15.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|15.4|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|15.4|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||12.6|18.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|15.9|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|16.9|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|19.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|23.2|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All|32.2|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|5.7|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|6.6|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.0|8.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|7.1|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|7.4|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|7.7|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|8.5|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.2|11.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|8.6|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|9.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|9.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|10.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|11.3|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|11.7|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|12.5|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|12.8|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|14.3|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|14.3|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||11.7|17.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|16.8|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|17.6|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|18.1|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|18.4|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|21.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|24.3|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All|30.4|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2012|Male|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|6.9|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|8.8|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|9.5|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||7.6|11.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|9.7|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|10.0|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.5|12.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|10.4|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|10.4|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|10.8|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|11.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|11.6|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|11.6|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|13.5|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|13.7|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|13.9|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|14.2|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||12.5|15.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|14.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|15.2|U.S. Total, U.S. Total|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Foodborne Diseases Active Surveillance Network (FoodNet) Table 2b Incidence of culture-confirmed bacterial and laboratory-confirmed parasitic infections, and postdiarrheal hemolytic uremic syndrome (HUS), by year and pathogen, Foodborne Diseases Active Surveillance Network (FoodNet), United States, 1996_2014*||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|16.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.0|18.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|16.9|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|18.9|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|23.0|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|23.4|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|All|28.3|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|American Indian/Alaska Native|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|American Indian/Alaska Native|21.6|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|American Indian/Alaska Native|44.3|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|0.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|4.9|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|5.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|9.3|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|10.4|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|10.5|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|11.1|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|14.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|15.8|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|21.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||16.8|26.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|21.1|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI|27.3|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|2.3|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|4.8|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|6.1|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|6.6|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|6.7|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|6.9|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|7.2|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|8.7|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|10.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|10.5|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||6.2|16.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|10.8|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|11.6|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.7|16.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|13.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|15.4|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|17.0|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|17.9|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|18.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|21.8|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black|31.8|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Black||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|0.7|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|4.9|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|5.1|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|5.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|10.1|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|10.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|11.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|12.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|13.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|14.1|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|14.2|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|14.6|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|14.9|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|15.5|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||11.7|20.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|24.6|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic|27.7|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Multiracial|7.9|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Multiracial|23.2|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other|3.3|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other|14.5|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other|17.4|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other|25.4|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other|496.7|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|Other||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|3.8|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|4.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|5.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|7.3|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|7.4|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|8.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|8.3|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||5.9|11.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|8.8|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.8|11.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|8.9|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||6.6|11.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|10.4|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|10.8|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|12.0|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|12.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|13.0|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|13.5|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|14.0|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|19.4|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|19.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|26.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Both|White|27.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|6.3|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|6.9|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|9.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|10.5|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||7.7|13.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|10.7|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|10.8|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|10.8|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|11.7|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.9|15.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|12.3|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|12.7|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|13.6|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|13.7|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|13.8|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|14.3|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|15.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||12.3|18.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|15.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|15.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|16.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||14.1|19.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|18.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|22.6|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|23.2|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Female|All|29.8|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|5.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|6.2|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|6.8|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|6.8|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|7.9|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|8.2|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||4.9|11.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|9.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|9.3|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|9.5|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|9.6|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|10.1|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|11.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||9.6|13.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|12.2|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|13.2|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|13.5|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|14.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|17.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||14.2|20.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|18.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|19.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|22.7|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|23.5|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|25.0|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All|26.7|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2013|Male|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|6.1|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.0|7.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|7.1|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|7.8|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|8.1|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.9|10.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|9.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|10.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|11.2|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||9.1|13.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|11.5|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|12.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|12.7|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|13.1|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|13.2|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|13.4|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|14.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|14.7|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|15.3|U.S. Total, U.S. Total|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Foodborne Diseases Active Surveillance Network (FoodNet) Table 2b Incidence of culture-confirmed bacterial and laboratory-confirmed parasitic infections, and postdiarrheal hemolytic uremic syndrome (HUS), by year and pathogen, Foodborne Diseases Active Surveillance Network (FoodNet), United States, 1996_2014*||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|16.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|17.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley). |||15.1|19.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|19.7|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|22.3|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|25.8|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|26.1|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|All|78.9|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||15.9|19.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|American Indian/Alaska Native|0.0|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|American Indian/Alaska Native|14.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|American Indian/Alaska Native|18.5|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|American Indian/Alaska Native|74.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|0.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|4.9|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|5.9|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|6.2|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|6.3|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|7.0|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|7.2|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|7.7|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|10.1|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|16.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||5.6|26.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI|19.1|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|3.0|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|5.0|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|7.3|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|8.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|8.7|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|10.3|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|11.3|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|11.6|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.7|16.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|11.9|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|12.5|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|13.8|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|13.9|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|14.3|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|16.0|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|17.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|23.3|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black|32.1|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Black||San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|0.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|3.7|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||2.6|4.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|6.9|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|9.5|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|9.5|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|10.6|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|11.2|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|12.1|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|13.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|13.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|17.3|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|22.0|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic|28.2|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Multiracial|27.1|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other|7.2|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other|8.3|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other|341.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other|413.9|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|Other||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|1.8|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|5.2|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|5.9|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|7.1|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|7.4|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|9.2|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|9.4|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||7.1|12.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|11.4|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|11.8|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|12.3|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|12.7|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|13.7|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|15.7|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||12.4|19.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|17.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|22.5|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|23.9|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|24.5|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White|31.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|4.2|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|6.9|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.3|8.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|8.0|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|8.2|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|9.8|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.3|13.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|10.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|10.3|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|10.7|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||7.9|14.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|12.4|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|12.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|12.6|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|13.0|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|13.3|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|14.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|14.4|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|15.1|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|15.8|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Change in data system in 2014; 40% of cases had race/ethnicity unknown|||13.1|19.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|17.0|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|18.5|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||15.8|21.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|19.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|20.8|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|22.8|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|23.1|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Female|All|24.2|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|3.0|Washington, DC|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|http://planning.dc.gov/sites/default/files/dc/sites/op/publication/attachments/Atlas%202010%20RGB%20color_part1.pdf|DC. Gov search for 2010 US Census Data|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|5.4|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||3.9|6.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|6.3|Cleveland, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|7.0|Phoenix, AZ|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|7.4|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|9.4|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Salmonella cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|10.6|Los Angeles, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|10.8|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|11.5|Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data|||||8.5|15.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|11.9|Long Beach, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|12.0|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|13.8|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|14.0|Chicago, Il|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|14.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|14.7|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|16.0|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|16.6|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|17.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||14.5|19.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|18.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Change in data system in 2014; 40% of cases had race/ethnicity unknown|||15.6|22.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|20.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|23.8|San Jose, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|27.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All|29.1|Baltimore, MD|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System or NEDSS|Crude rate per 100,000|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2014|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|8.4|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|9.2|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||7.8|10.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|10.8|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease||Data from the most recent year are not final and are subject to change.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|11.7|Kansas City, MO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|12.2|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|12.5|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|12.7|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|14.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|15.5|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||13.7|17.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|17.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|19.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Change in data system in 2014; 30% of cases had race/ethnicity unknown. Also, first year of receiving reports via electronic lab reporting and increased use of culture independent diagnostic tests in regional laboratories, leading to increase in detection compared to previous years|||17.4|22.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|21.2|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||17.5|24.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|22.1|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All|23.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|All||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|0.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|3.1|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|7.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|8.6|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||1.1|16.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|11.8|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|13.0|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI|34.6|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI||Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|5.8|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|7.7|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||3.2|12.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|7.8|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|9.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|10.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|12.1|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.1|17.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|12.1|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|12.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|16.8|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|23.7|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black|25.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||11.1|39.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|4.6|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|10.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|11.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|11.1|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||9.2|13.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|13.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|17.4|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||4.5|30.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|18.9|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|21.8|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic|21.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic||Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Other|0.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Other|9.3|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Other|34.5|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||14.1|54.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|Other||San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|4.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|5.6|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|8.5|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||5.5|11.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|12.4|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|13.0|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|15.0|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||11.8|18.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|15.3|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|19.6|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||15.3|23.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|20.1|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|21.4|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White|35.6|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Both|White||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|8.0|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|9.9|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||7.9|11.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|11.4|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.6|15.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|13.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|13.7|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|14.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|15.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|15.7|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||13.2|18.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|18.7|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|19.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Change in data system in 2014; 30% of cases had race/ethnicity unknown. Also, first year of receiving reports via electronic lab reporting and increased use of culture independent diagnostic tests in regional laboratories, leading to increase in detection compared to previous years|||16.1|22.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|22.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||17.3|28.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|23.3|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All|26.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|8.5|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||6.6|10.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|8.9|Detroit, MI|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|10.5|New York City, NY|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease||Data from the most recent year are not final and are subject to change.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|10.9|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.1|14.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|11.1|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|11.3|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|11.7|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|14.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|15.2|San Antonio, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||Bexar County level data|||12.7|17.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|16.3|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|19.7|Seattle, WA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|||||14.7|24.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|20.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Change in data system in 2014; 30% of cases had race/ethnicity unknown. Also, first year of receiving reports via electronic lab reporting and increased use of culture independent diagnostic tests in regional laboratories, leading to increase in detection compared to previous years|||17.0|23.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|20.8|Boston, MA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All|20.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2015|Male|All||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|7.2|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||6.0|8.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|10.7|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|11.9|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query|||||9.6|14.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|12.1|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||9.4|14.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|12.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|13.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|14.9|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|All|16.2|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Asian/PI|4.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Asian/PI|5.6|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Asian/PI|8.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Asian/PI|14.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black|2.1|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black|8.6|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black|8.7|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black|9.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black|10.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black|14.2|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||8.8|19.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Hispanic|5.9|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Hispanic|11.2|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Hispanic|16.2|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Hispanic|16.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Hispanic|22.0|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query|||||13.5|34.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Other|0.0|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|3.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|8.7|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|10.3|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query|||||8.0|13.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|11.3|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.8|14.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|15.4|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|16.8|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Both|White|21.4|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|7.6|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.9|9.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|10.6|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|11.2|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|12.7|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||8.7|16.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|13.1|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query|||||9.8|17.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|15.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|16.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Female|All|16.3|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|6.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.||||||5.1|8.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|10.6|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|OPHAT Salmonella query|||||7.6|14.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|10.8|Houston, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|11.0|Denver, CO|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|11.5|Columbus, OH|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||7.7|15.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|13.1|Dallas, TX|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|13.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Salmonella (Per 100,000 people)|2016|Male|All|16.1|San Diego County, CA|Rate of lab-confirmed infections caused by Salmonella per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Salmonellosis, (Non-Thypoid/Non-Paratyphoid) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|0.3|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|0.3|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|0.4|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data|||0.1|0.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|0.6|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.3|1.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|0.6|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|1.0|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|1.4|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County.  includes shiga toxin in feces. Numerator too small to provide stable rates, therefore not calculated|||0.9|2.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|1.5|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|1.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|2.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||1.1|3.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All|3.3|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Asian/PI||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Asian/PI||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Asian/PI||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Asian/PI||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black|0.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black|0.0|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black|0.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black||Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Black||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic|0.0|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic|0.3|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic|1.1|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic|2.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic|2.1|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic||San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Hispanic||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Other|0.0|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Other||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|Other||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White|0.7|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White|1.3|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White|1.5|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||0.3|2.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White|1.9|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White|5.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Both|White||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|0.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|0.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.2|1.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|0.8|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|1.1|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|1.4|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||0.7|2.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|1.6|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|2.0|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||0.4|3.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|2.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All|4.1|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Female|All||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.5|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.1|1.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.7|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.7|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.8|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|0.9|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|1.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|1.5|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||0.7|2.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|2.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All|2.6|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||0.8|4.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2010|Male|All||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|0.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|0.5|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|0.6|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|0.7|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|1.0|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|1.1|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|1.2|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.7|1.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|2.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|2.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|2.3|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||1.3|3.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|2.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.8|3.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|2.8|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All|3.6|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||2.1|5.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Asian/PI||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Asian/PI||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Asian/PI||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Asian/PI||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black|0.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black|1.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black|10.6|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||1.3|19.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic|0.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic|0.4|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic|1.3|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic|1.6|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic|3.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.4|5.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Hispanic||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Other||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Other||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|Other||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|0.3|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|0.4|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|1.1|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|2.6|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|2.6|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||1.4|4.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|3.2|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||1.5|5.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White|4.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.5|6.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Both|White||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.5|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.5|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.8|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|0.9|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|1.2|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|1.8|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.9|2.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|2.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|2.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|3.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||1.3|5.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|3.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.4|5.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All|4.2|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.5|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.5|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.6|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.2|1.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|0.9|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|1.1|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|1.4|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|1.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||0.8|2.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|2.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All|4.0|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||1.7|6.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2011|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.6|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.7|Chicago, Il|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|0.9|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.0|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.1|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.1|U.S. Total, U.S. Total|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Escherichia coli O157:H7 infections commonly transmitted through food (per 100,000 population), Foodborne Diseases Active Surveillance Network (FoodNet), CDC/NCID||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.4|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.5|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||1.0|2.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.6|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.7|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.9|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|1.9|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|2.5|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.8|3.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|2.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|3.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All|4.2|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.7|6.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|American Indian/Alaska Native|6.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI|0.4|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI|1.7|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI|2.0|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Asian/PI||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|0.0|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|0.3|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|0.4|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|0.6|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|1.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black|3.6|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|0.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|0.5|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|0.5|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|1.0|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|1.0|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|1.3|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|1.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic|2.5|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Multiracial|3.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Other|10.9|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Other||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|0.3|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|0.4|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|1.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|1.1|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|1.1|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|1.5|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|2.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|2.0|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|2.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|2.7|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|3.7|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.2|6.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|4.1|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White|4.7|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.9|7.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Both|White||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.6|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.8|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.9|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|0.9|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|1.1|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|1.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|1.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|1.4|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|1.5|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.8|2.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|1.7|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|2.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|2.6|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|2.7|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|3.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.0|4.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|3.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All|3.8|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.0|6.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|0.3|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|0.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|0.4|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.0|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; crude rates per 100,000 population|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.0|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.1|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.4|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.6|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.8|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.9|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|1.9|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.0|3.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|2.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|3.2|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|3.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All|4.7|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.6|7.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2012|Male|White|5.7|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||1.6|3.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|0.4|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|0.4|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|0.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|0.7|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|0.8|Chicago, Il|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.0|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.1|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.2|U.S. Total, U.S. Total|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Escherichia coli O157:H7 infections commonly transmitted through food (per 100,000 population), Foodborne Diseases Active Surveillance Network (FoodNet), CDC/NCID||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.7|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|1.9|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|2.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.8|3.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|2.7|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|3.4|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|3.6|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.3|5.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All|3.6|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|American Indian/Alaska Native|0.0|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||American Indian alone||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI|2.1|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI|2.6|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI|4.2|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Asian/PI||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|0.3|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|0.8|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|0.8|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|1.2|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|1.9|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|3.4|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black|7.3|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|0.5|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|0.9|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|1.0|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|1.4|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|1.4|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|1.6|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic|3.8|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Multiracial|3.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other|2.1|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other|7.2|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other|82.8|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|0.2|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|0.6|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|0.7|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|0.7|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|1.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|1.4|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|1.8|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|2.5|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|2.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|3.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|3.2|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|3.4|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.1|5.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White|3.9|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.3|6.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Both|White||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|0.4|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|0.4|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|0.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|0.7|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|1.0|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|1.2|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|1.3|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.6|2.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|1.3|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|1.4|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|1.5|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|2.1|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|2.1|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|2.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|2.4|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|3.2|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|3.3|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.1|4.9
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|3.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|4.8|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.8|7.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All|5.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Female|White|5.0|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||2.7|8.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.2|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.4|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.4|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.5|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.5|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.5|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.8|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.3|1.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.8|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|0.9|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|1.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|1.0|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NEDSS Base System|Used confirmed cases only as indicator of laboratory-confirmed; did not include probable cases (of note there were many cases with unknown race/ethnicity)|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|1.2|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|1.3|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|1.6|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|2.0|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|3.6|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All|4.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.0|3.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2013|Male|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.2|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.3|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.4|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.4|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data|||0.1|0.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.7|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.8|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.9|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.9|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|0.9|U.S. Total, U.S. Total|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Escherichia coli O157:H7 infections commonly transmitted through food (per 100,000 population), Foodborne Diseases Active Surveillance Network (FoodNet), CDC/NCID||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|1.0|Chicago, Il|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|1.1|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|1.7|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|2.4|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Other Communicable Disease case reports, National Center for Health Statistics Population Estimates(Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|Age-Adjusted Rate per 100,000 Persons. If the Relative Standar Error >= 30%, then the estimate was supressed|OPHAT, communicable disease query|||1.4|3.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|2.6|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|2.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|3.1|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|3.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|3.4|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|3.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.7|4.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All|3.8|Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||2.3|5.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI|0.0|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI|1.0|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI|1.7|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Asian/PI||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|0.0|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|0.0|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|0.2|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|0.3|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|1.7|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|5.1|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black|10.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black||Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|0.7|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|0.8|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|1.1|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|2.5|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|2.6|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|3.6|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|4.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.2|6.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic|5.4|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Hispanic||San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Multiracial|3.9|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Other|14.5|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Other||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Other||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|Other||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|0.0|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|1.0|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|1.3|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|2.2|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||1.1|4.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|2.6|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|2.6|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|2.7|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|3.0|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White|5.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.3|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.3|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.3|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.4|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.4|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.2|1.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|0.8|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|1.1|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|2.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|2.5|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|2.6|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|2.6|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|3.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.0|4.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|3.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All|4.3|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.0|Cleveland, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System (Ohio Department of Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.1|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|National Electronic Disease Surveillance System Base System (NBS) download at May 22, 2015.|Crude rate of confirmed Shiga toxin producing E. Coli cases in Houston City.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.3|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDSS||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.4|Long Beach, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Source: California Reportable Disease Information Exchange (CalREDIE), 2011-2014.||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.5|Washington, DC|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.6|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.1|1.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.7|Phoenix, AZ|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|2010 US Census||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.9|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|0.9|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|1.0|Los Angeles, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Download disease from Visual CMR database, LAC DPH/ACDC.|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. LA City identified by census tract of residence of decedent.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|1.1|Miami (Miami-Dade County), FL|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|2.5|San Jose, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Dieases Information Exchange (CalREDIE), 2012-2014, data are provisional as of April 30, 2015; U.S. Census Bureau, Census 2010||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|2.6|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Texas DHSH- NEDSS query|Crude rates|All data are county level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|2.9|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|3.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Colorado Electronic Disease Reporting System (CEDRS)|All reportable conditions diagnosed in Colorado are reported to CEDRS; CEDRS data are available electronically to local health departments for surveillance and so cases may be interviewed.|||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|3.6|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Public Health-Seattle & King County; Communicable Disease Epidemiology and Immunization Section. 2010 Census.||used 2010 census as denominator||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All|4.1|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.7|5.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All||Boston, MA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2014|Male|All||Indianapolis (Marion County), IN|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MCPHD Foodborne Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|0.6|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|0.8|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data|||0.4|1.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|1.1|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.7|1.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|1.2|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|1.5|Kansas City, MO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|MDHSS WebSurv - Communicable Disease Registry for the State of Missouri||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|2.1|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|2.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|2.8|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|4.3|Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||2.7|6.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|4.9|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||3.8|6.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All|5.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||3.4|7.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|All||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI|0.0|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI|4.2|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI|9.8|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Asian/PI||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black|0.6|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black|0.6|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black|1.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black|2.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Black||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|0.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|0.7|San Antonio, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Bexar County level data|||0.2|1.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|1.5|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|1.7|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|2.0|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|2.1|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic|8.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||5.3|11.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Other|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Other||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed here due to small numbers; Includes shiga toxin in feces. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|Other||Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here due to low case count.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|0.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|1.2|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|3.3|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|4.5|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|4.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.8|7.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White|5.0|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||2.8|7.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Both|White||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|0.8|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|1.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|1.1|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|1.3|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.6|2.1
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|1.5|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|2.3|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|3.2|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|5.6|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||4.1|7.6
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All|7.2|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||4.2|10.2
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|0.0|Detroit, MI|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|0.1|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|0.9|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.3|1.5
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|1.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|1.3|New York City, NY|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Communicable Disease||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|1.3|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|1.9|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|2.4|Fort Worth (Tarrant County), TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|3.1|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT Communicable Disease query|||||1.6|5.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|3.3|Seattle, WA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance Database|Includes Confirmed, Probable, & Suspect STEC||||1.3|5.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All|4.0|Oakland (Alameda County), CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|CCR Title 17 reportable diseases reported to ACPHD Acute Communicable Disease Unit|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley).  Data includes shiga toxin in feces|||2.7|5.8
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2015|Male|All||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All|0.8|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All|1.5|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All|1.7|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||1.2|2.3
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All|1.8|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All|3.0|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All|5.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Asian/PI|0.0|Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Asian defined as Asian only. ||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Asian/PI|0.0|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Asian/PI|0.8|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Asian/PI|1.0|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Asian/PI|1.3|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black|0.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black|2.7|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black|6.9|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Black||San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic|0.5|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic|1.6|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic|2.1|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic|3.1|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic|7.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White|1.5|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White|1.8|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White|3.6|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White|4.1|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query|||||2.7|6.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White|8.0|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Both|White||Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|0.8|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|1.6|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|1.7|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|1.9|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||1.0|2.7
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|3.4|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|4.3|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All|5.2|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query|||||3.2|8.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Female|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|0.8|Houston, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|1.3|Philadelphia, PA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|1.6|Las Vegas (Clark County), NV|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.||||||0.8|2.4
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|1.9|San Diego County, CA|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Shiga toxin-Producing E. coli (including O157) confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|2.7|Dallas, TX|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|2.8|Portland (Multnomah County), OR|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|OPHAT E Coli Query|||||1.4|5.0
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All|5.7|Denver, CO|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|||||||
Food Safety|Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people)|2016|Male|All||Columbus, OH|Rate of lab-confirmed infections caused by Shiga Toxin-Producing E-Coli per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|2.3|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||1.6|3.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|3.6|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|9.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||6.2|12.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|9.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report, Vol 24, 2012 -- http://www.cdc.gov/hiv/pdf/statistics_2012_HIV_Surveillance_Report_vol_24.pdf||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|9.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|10.8|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||9.4|12.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|11.0|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||Bexar County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|14.1|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|15.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||13.0|18.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|17.6|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||16.3|18.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|19.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|19.7|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|22.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|26.4|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|27.7|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|All|57.9|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|American Indian/Alaska Native|5.4|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||American Indian alone; May include those of Hispanic origin||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|1.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.0|8.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|3.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|3.2|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|3.3|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1.6|4.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|4.9|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|5.4|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||Does not include Pacific Islander||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|6.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|6.8|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||2.9|10.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|7.5|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI||San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; 1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|5.6|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|18.8|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||11.5|29.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|29.3|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|34.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||27.3|41.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|34.6|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|36.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|36.7|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||May include those of Hispanic origin||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|43.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|43.8|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||37.5|50.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|44.7|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|58.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||20.8|35.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|65.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|78.5|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black|79.1|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Black||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|3.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|7.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||2.8|14.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|9.4|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||3.3|15.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|10.2|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||7.6|12.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|11.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||Hispanic of any race, not Hispanic as race||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|12.6|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|16.0|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|16.1|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|16.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|17.3|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||15.5|19.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|38.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|41.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|44.4|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|6.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.2|33.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|6.7|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||0.8|12.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|8.1|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|11.5|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|19.4|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||Multiple races||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|33.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|43.0|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other|53.9|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|Other||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|3.1|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|3.7|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||May include those of Hispanic origin||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|5.2|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|5.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||2.9|8.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|5.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||2.2|12.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|7.5|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|8.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|8.7|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||6.8|10.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|10.1|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|13.0|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||10.9|15.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|15.8|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|17.1|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|19.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White|41.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Both|White||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|0.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|1.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|2.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.5|5.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|3.7|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|3.7|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||2.5|4.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|3.9|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|4.9|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||3.9|5.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|5.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 24 2012. Table 2b. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2008-2012 - United States and 6 dependent areas||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|8.0|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|9.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.2|12.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|11.8|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|14.8|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|15.7|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All|41.7|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Female|All||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|3.9|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||2.6|5.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|6.4|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|16.4|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||11.1|23.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|17.1|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 25 2013. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumalative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|17.8|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||15.2|20.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|18.0|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|20.6|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|22.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||17.9|27.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|30.4|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||28.0|32.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|33.4|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|35.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|35.7|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|37.8|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|42.2|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2010|Male|All|76.5|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|2.3|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||1.6|3.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|2.9|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|8.0|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|9.0|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report, Vol 24, 2012 -- http://www.cdc.gov/hiv/pdf/statistics_2012_HIV_Surveillance_Report_vol_24.pdf||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|9.2|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|9.5|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||7.4|12.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|10.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||8.6|11.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|10.8|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|10.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||Bexar County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|11.0|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|13.8|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|13.8|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||12.6|14.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|16.3|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|16.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||13.6|19.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|17.2|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|18.9|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|21.4|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|21.5|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|27.0|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|27.7|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|28.4|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|58.2|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|All|66.3|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||American Indian alone; All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|82.4|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|American Indian alone||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|0.0|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|2.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|3.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|3.3|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1.6|4.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|4.4|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|4.4|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|4.5|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||1.4|7.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|4.9|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|10.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|12.7|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|9.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|11.1|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|15.5|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|20.2|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|21.6|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|25.7|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|26.5|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|30.0|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|31.5|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||26.1|36.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|33.7|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||26.6|40.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|34.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||26.6|43.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|38.9|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|43.7|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|45.7|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|63.6|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|65.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|78.4|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black|84.8|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Black||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|3.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|5.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|7.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||1.9|12.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|9.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||6.5|11.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|9.2|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|10.1|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|12.4|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||10.9|14.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|12.4|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|14.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|15.1|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|15.7|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|16.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|17.6|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|28.8|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|31.0|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|33.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|67.9|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Multiracial|0.0|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Multiracial|4.0|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Multiracial|7.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Multiracial|18.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other|3.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other|11.5|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other|18.4|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other|34.0|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.8|61.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other|44.7|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|Other||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|2.3|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|3.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|5.2|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|6.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||5.3|8.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|7.0|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||4.1|9.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|7.2|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|7.5|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|8.3|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|9.0|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|9.3|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|9.8|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||7.4|12.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|10.7|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|11.1|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|12.1|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||10.1|14.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|12.3|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|12.4|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|15.3|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|16.9|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White|20.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Both|White||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|0.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|1.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|2.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|2.9|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||2.2|3.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|3.8|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||2.6|5.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|4.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|4.1|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|4.2|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|5.5|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|5.6|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|5.7|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|7.7|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||4.9|10.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|9.7|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|10.4|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|14.1|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|14.9|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|18.4|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All|40.2|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Female|All||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|3.9|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||2.6|5.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|5.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|14.5|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|16.1|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||13.6|18.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|16.6|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|16.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|18.1|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||14.1|23.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|18.2|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|23.8|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|24.7|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||22.5|26.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|26.0|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||20.7|31.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|27.5|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|27.8|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|30.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|33.9|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|37.0|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|39.1|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|40.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|42.7|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2011|Male|All|79.0|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|2.1|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|2.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|6.0|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|7.6|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||5.8|9.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|7.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|8.5|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|8.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||Bexar County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|8.9|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 24 2012. Table 2b. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2008-2012 - United States and 6 dependent areas||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|9.1|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|10.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|11.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|12.3|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|13.1|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||10.9|15.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|15.3|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||14.1|16.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|15.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|17.4|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|18.0|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|19.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|19.9|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|21.7|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|24.4|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|27.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||23.6|31.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|28.2|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|29.8|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|68.0|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|All|72.3|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|25.6|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|2.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|2.6|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|2.8|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|3.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|4.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|4.6|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|5.6|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||3.5|7.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|6.3|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|7.3|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|8.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|9.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|14.4|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|7.3|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|10.1|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|11.7|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|17.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|22.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|26.0|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||19.6|32.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|27.9|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|28.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|30.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|32.4|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|32.7|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|32.9|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||27.4|38.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|36.4|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 24 2012. Table 4a. Stage 3 (AIDS), by race and selected characteristics, 2012 - United States||May include those of Hispanic origin||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|37.2|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|38.5|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|45.5|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|55.0|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|56.0|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|59.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||50.4|69.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|63.3|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|75.9|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|85.5|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|101.1|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Black|111.3|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|2.1|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|2.9|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|8.3|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|9.1|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|9.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|10.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|10.2|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 24 2012. Table 4a. Stage 3 (AIDS), by race and selected characteristics, 2012 - United States||Hispanic of any race, not Hispanic as race||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|11.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|11.7|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|13.6|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.2|21.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|14.4|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||7.5|25.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|14.5|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|15.1|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||13.4|16.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|15.2|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|16.0|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|16.3|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|20.0|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|26.0|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|26.4|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|28.5|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|34.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|41.1|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|49.3|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Multiracial|0.8|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Multiracial|14.2|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Multiracial|33.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|2.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|9.4|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|18.4|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|23.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|32.4|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|52.1|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|56.7|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||21.6|91.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other|89.4|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|Other||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|1.8|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|2.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|3.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report Vol 24 2012. Table 4a. Stage 3 (AIDS), by race and selected characteristics, 2012 - United States||May include those of Hispanic origin||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|4.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|5.2|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|5.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|5.9|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|6.1|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|7.2|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||5.1|9.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|7.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||4.4|10.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|7.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|8.3|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.3|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.5|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.6|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|11.6|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|11.9|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||9.9|13.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|13.0|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|13.4|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|14.0|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|15.5|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|18.5|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White|36.2|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Both|White||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|0.5|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|1.3|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|1.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|1.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|1.9|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|2.2|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.1|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||2.4|3.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.6|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.7|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|4.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.5|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.7|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|6.3|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|7.4|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|8.7|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|12.2|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|12.3|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|15.1|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||11.2|19.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|16.1|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|41.9|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All|46.9|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|3.4|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|4.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|10.1|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|13.7|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|13.9|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|13.9|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||10.4|18.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|16.3|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|16.4|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|22.2|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|22.4|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||18.0|26.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|23.8|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|27.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|27.6|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||25.3|29.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|28.6|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|29.4|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|30.0|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|33.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|35.4|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|36.0|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|37.7|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|40.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||33.8|47.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|41.7|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|52.6|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|93.9|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2012|Male|All|101.7|Baltimore, MD|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent AIDS cases diagnosed >= 13 years of age||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|2.4|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|3.3|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|5.4|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|7.7|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||5.9|9.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|8.0|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|8.7|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|8.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|8.9|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|8.9|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||Bexar County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|9.3|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|10.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|10.9|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||9.0|13.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|11.4|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|12.7|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||11.6|13.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|13.7|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|15.0|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|15.7|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|17.3|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|18.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|19.8|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|23.0|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|23.3|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|26.4|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|32.0|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||27.9|36.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|All|49.9|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|4.0|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|15.3|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|1.5|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|1.6|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|2.3|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|2.3|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||Asian alone||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|2.8|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|4.4|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|5.0|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||3.0|7.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|6.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|7.1|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|7.3|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|9.8|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|10.1|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|15.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|17.1|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|18.2|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|18.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|20.6|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|21.5|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||15.8|27.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|23.4|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|25.7|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||20.8|30.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|25.7|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|30.2|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|31.2|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|32.8|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|35.3|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|38.4|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|38.4|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|47.9|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|52.8|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|67.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||57.7|78.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|68.4|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|75.2|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Black|81.0|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|3.5|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|4.1|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|8.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|8.5|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|9.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|9.7|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|10.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|10.4|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|11.7|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|12.1|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|12.6|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||11.0|14.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|13.6|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.2|21.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|17.9|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|18.8|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||10.8|30.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|23.5|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|25.1|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|27.9|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|32.9|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|36.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|6.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|9.8|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|16.7|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|18.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|27.4|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|5.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|5.2|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||Native Hawaiian or other Pacific Islander||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|5.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|9.7|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|11.7|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|13.7|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|33.9|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|Other|62.4|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||25.5|99.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|2.6|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|3.2|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|4.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|5.9|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|6.0|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|6.2|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|6.4|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||4.4|8.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|6.4|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|6.5|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|7.0|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|7.2|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|7.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|8.3|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|8.5|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|8.6|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|9.5|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||7.8|11.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|10.4|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|10.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||7.4|14.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|10.9|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|13.4|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|14.9|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|15.8|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White|26.7|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Both|White||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|0.9|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|1.7|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.3|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1.7|3.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.3|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.6|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|3.3|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|3.4|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|3.8|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|4.3|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|4.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|4.8|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|6.5|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|9.2|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|9.9|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|14.5|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|15.7|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|19.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||14.9|23.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All|29.6|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|4.1|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|5.6|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|9.9|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|14.3|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||10.7|18.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|14.9|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|15.2|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|15.3|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|16.3|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|16.3|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|DSHS, 2015||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|17.0|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||13.2|20.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|18.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|19.3|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|23.1|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||21.0|25.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|23.7|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|24.9|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|25.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|26.9|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|32.7|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|33.6|New York City, NY|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|33.9|Chicago, Il|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|34.9|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|2011-2013 AACO Surveillance Report||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|39.1|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|41.1|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|45.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||38.5|52.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2013|Male|All|70.0|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|3.0|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|3.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|4.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, 2016 Q2||Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||2.9|7.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|5.5|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|6.3|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|6.6|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|7.1|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|7.2|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||Bexar County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|7.6|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||5.8|9.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|9.2|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|9.4|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|10.4|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||9.4|11.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|10.5|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|10.5|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|10.6|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|numerators from National HIV Surveillance System, WA state data specific for Seattle residents||||8.3|13.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|12.1|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||10.1|14.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|13.0|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|14.5|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|16.1|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|17.4|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|19.8|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|20.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||17.6|24.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|All|37.6|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|4.0|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|1.5|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|1.6|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|2.0|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||0.7|3.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|2.1|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||Asian alone||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|3.2|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|8.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|9.4|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|10.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.2|18.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|5.3|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|6.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|8.8|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|10.3|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, 2016 Q2||Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||5.1|18.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|11.9|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|14.3|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|15.0|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|22.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|22.5|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||18.0|27.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|23.7|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||17.7|29.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|25.4|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|25.8|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|29.8|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|35.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||21.8|64.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|40.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|43.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||35.1|51.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|56.6|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|58.1|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Black|59.9|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|2.1|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|3.6|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|6.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, 2016 Q2||Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||2.2|13.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|7.6|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|7.7|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|8.3|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|8.5|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|8.5|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|8.6|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|10.7|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|10.8|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||9.4|12.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|12.9|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|14.9|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|15.7|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||7.7|23.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|24.6|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|25.6|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Multiracial|12.6|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|0.0|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|3.5|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||Native Hawaiian or other Pacific Islander||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|3.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|4.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|8.4|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|17.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|18.5|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|24.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||12.4|43.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other|24.7|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|Other||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|2.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, 2016 Q2||Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||0.2|7.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|2.4|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|2.7|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|2.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|5.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|5.5|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|5.9|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|6.0|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|6.3|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|6.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|6.7|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||4.7|9.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|7.0|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||4.1|9.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|7.7|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||6.1|9.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|8.1|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.7|11.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|8.2|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|9.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|9.5|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|11.0|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|13.4|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White|13.9|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|0.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|1.1|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|1.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|1.9|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.8|3.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|2.1|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|2.3|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|2.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, 2016 Q2||Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||0.8|5.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|2.7|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|2.7|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||2.0|3.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.3|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.8|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.8|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|5.3|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|7.9|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|9.4|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|10.3|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|10.7|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|11.9|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||8.4|15.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All|18.9|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|5.0|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|5.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|7.4|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, 2016 Q2||Only includes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||4.0|12.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|9.9|San Jose, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|10.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|11.0|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|12.0|U.S. Total, U.S. Total|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|HIV Surveillance Report 2014. Table2a. Stage 3 (AIDS), by year of diagnosis and selected characteristics, 2009-2013 and cumulative - United States||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|12.4|Long Beach, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|13.0|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||9.7|17.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|14.0|Minneapolis, MN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|All new cases of AIDS diagnosed within a given calendar year, including AIDS at first diagnosis||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|15.1|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|16.3|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ehars||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|18.1|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||16.2|19.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|18.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|18.7|Indianapolis (Marion County), IN|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|MCPHD HIV/AIDS Data|||||14.7|22.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|19.4|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||15.0|24.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|21.2|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|22.9|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|25.4|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|29.4|Miami (Miami-Dade County), FL|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS V4.7||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|30.4|San Francisco, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|30.6|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||24.8|36.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2014|Male|All|56.3|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|2.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|3.8|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||386.2|414.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|3.9|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||3.0|4.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|6.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||3.9|9.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|6.1|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|7.7|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||Bexar County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|8.3|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|8.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||6.3|10.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|9.5|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||8.2|10.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|10.2|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|13.1|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|16.4|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|18.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|18.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||15.3|21.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|All|32.4|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|American Indian/Alaska Native|207.4|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||139.9|295.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|2.4|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|3.4|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|3.7|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1.9|5.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|4.5|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||1.4|7.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|4.9|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|5.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|7.4|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||2.4|17.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|125.3|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||100.5|154.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|7.9|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|8.4|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||1.6|16.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|9.1|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||5.5|12.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|16.2|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|16.7|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|19.1|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|19.9|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|21.3|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||16.9|25.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|22.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|27.9|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|30.2|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||18.2|47.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|30.3|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||22.6|38.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|35.5|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|37.1|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|41.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||33.3|49.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|53.1|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black|510.0|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||451.6|573.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|2.1|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|3.3|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|5.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||1.6|11.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|6.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|7.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|8.6|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||6.2|11.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|9.0|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||7.6|10.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|10.2|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|11.5|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||4.7|18.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|12.8|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|13.8|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||6.1|27.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|17.1|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|31.1|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|395.6|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||355.4|439.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|1.4|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|3.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|5.8|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|11.0|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|26.0|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|27.7|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other|28.4|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||3.5|53.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|Other||Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|1.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|2.9|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||1.8|4.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|3.3|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||2.1|4.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|3.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||1.1|10.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|3.9|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|4.0|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|4.5|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|4.6|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||2.3|6.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|4.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|5.9|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|6.7|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|6.8|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.7|9.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|7.1|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||5.4|8.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|7.8|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||6.3|9.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|8.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|8.9|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|14.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Both|White|360.2|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||345.7|375.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|0.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|1.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|1.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.3|4.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|1.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|2.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|2.1|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|2.2|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1.6|2.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|3.3|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.7|5.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|4.1|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||2.8|5.4
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|6.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|7.9|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|8.8|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|9.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|13.0|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||9.3|16.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|18.0|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All|70.4|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||62.4|79.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All||Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|4.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|6.5|Fort Worth (Tarrant County), TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Tarrant County Public Health|||||4.9|8.2
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|6.7|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||4.3|9.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|10.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|11.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||6.9|16.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|11.8|San Antonio, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Texas Department of State Health Services, Sexual Transmitted Disease Data||San Antonio TGA ||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|13.4|Seattle, WA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||9.8|17.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|14.8|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|17.5|Los Angeles, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||15.7|19.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|18.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|19.6|Houston, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|23.8|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|24.3|Charlotte, NC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||19.2|29.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|25.2|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|28.4|Detroit, MI|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|46.1|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2015|Male|All|736.6|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data from Jeff Capizzi, w/ OPHAT Tot Pop query|||||710.0|763.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|2.5|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||1.5|3.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|2.9|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|5.7|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|5.9|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||3.7|8.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|9.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|9.6|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|10.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||9.4|12.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|13.5|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|16.4|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|All|35.1|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|3.0|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|3.4|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|4.5|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.9|13.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|4.9|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|7.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||3.8|12.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|10.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|11.3|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||5.8|19.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|15.2|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|17.7|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|20.4|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|22.0|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|35.4|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||27.1|43.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|35.5|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black|57.5|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|3.5|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|4.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||1.1|10.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|6.8|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|10.0|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||7.4|12.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|10.5|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|12.8|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|17.6|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|27.4|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Other|2.7|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Other|3.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Other|6.1|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.2|33.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Other|13.5|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||5.1|21.8
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Other|23.0|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|1.7|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|2.9|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||1.7|4.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|3.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.6|8.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|4.1|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|4.4|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|5.5|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|6.6|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||5.0|8.3
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|8.9|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Both|White|10.5|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|0.8|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|1.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|2.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||0.5|5.1
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|3.7|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||2.5|4.9
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|4.2|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|7.9|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|7.9|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All|17.6|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|4.8|Portland (Multnomah County), OR|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||2.9|7.5
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|5.1|Phoenix, AZ|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|10.0|Oakland (Alameda County), CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Alameda County eHARS, Q2 2017|||||6.0|15.7
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|10.2|San Diego County, CA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1.Reported through 6/30/2017; 2.HIV diagnoses, regardless of stage of edisease.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|15.2|Kansas City, MO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|17.3|Denver, CO|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|17.9|Las Vegas (Clark County), NV|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.||||||15.3|20.6
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|19.7|Philadelphia, PA|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|AACO team||||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|25.2|Dallas, TX|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|AIDS Diagnoses Rate (Per 100,000 people)|2016|Male|All|52.8|Washington, DC|AIDS cases diagnosed in given year; crude rate per 100,000 population using 2010 US Census figure.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|7.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|14.6|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|17.3|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||15.5|19.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|17.4|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||15.4|19.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|18.3|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|18.5|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|23.5|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||19.0|28.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|29.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|32.8|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|34.6|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||32.8|36.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|38.4|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||33.9|42.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|41.9|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|49.0|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|All|77.6|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|American Indian/Alaska Native|8.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|4.5|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||0.9|13.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|5.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||Does not include Pacific Islander||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|6.7|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|7.1|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|9.8|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||6.9|12.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|10.7|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||5.9|15.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI|16.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Asian/PI||Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|18.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|30.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|37.1|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||26.4|47.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|46.9|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||34.8|61.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|49.8|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||39.9|59.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|51.8|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|54.8|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|56.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|56.8|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|78.5|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||69.9|87.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|83.3|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||72.1|94.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|91.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|108.3|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Black|133.3|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|7.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|16.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||13.5|20.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|17.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||10.0|27.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|17.9|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|18.1|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||15.5|20.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|18.8|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||10.1|27.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|23.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|24.6|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|25.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|31.0|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||28.6|33.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|32.4|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|59.1|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Hispanic|95.1|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|9.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||2.4|16.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|11.1|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|11.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||Native Hawaiian or other PI||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|18.1|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|23.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|36.4|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||13.3|79.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|42.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|51.0|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||17.7|84.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other|69.3|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|Other||San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|6.3|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|6.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|10.1|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||7.4|19.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|12.7|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||8.9|16.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|12.7|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||10.4|15.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|13.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|13.1|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|14.1|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|15.8|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||9.0|25.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|19.0|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|27.5|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|28.8|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|30.9|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||27.7|34.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Both|White|40.6|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|1.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|3.2|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|4.6|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|5.0|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||3.5|6.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|5.5|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||4.0|6.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|6.5|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||3.4|11.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|7.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|7.4|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|7.5|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|7.7|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||6.5|8.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|15.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|19.6|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||15.1|24.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|24.1|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Female|All|44.9|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|13.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|28.0|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|29.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||25.6|32.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|30.3|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||26.6|34.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|33.2|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|33.3|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|41.7|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||33.0|52.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|50.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|51.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|58.6|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||50.6|66.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|61.7|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||58.3|65.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|75.5|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|76.3|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2010|Male|All|115.2|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|8.0|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|14.3|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|15.8|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||1.6|18.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|16.2|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|17.6|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|17.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||16.1|19.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|19.5|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|19.7|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|25.1|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|26.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|28.3|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|30.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|30.6|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||28.9|32.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|31.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|32.5|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|37.3|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|37.3|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|39.0|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|40.1|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|41.3|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||36.6|46.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|50.1|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|81.4|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|All|122.0|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|5.1|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|7.8|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|5.7|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||Does not include Pacific Islander||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|6.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|8.7|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|8.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|9.3|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|9.5|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|9.6|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||6.7|12.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|10.3|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|13.3|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI|16.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||10.8|22.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Asian/PI||Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|28.2|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|32.4|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|36.5|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|39.4|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|41.3|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|42.4|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|45.8|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|51.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|51.8|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|52.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||42.2|62.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|54.6|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|57.1|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|61.9|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|63.2|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||55.6|70.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|67.5|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|77.2|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|80.2|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||69.1|91.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|80.3|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|91.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|107.4|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|120.4|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Black|181.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|9.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|15.9|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||8.5|27.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|16.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|17.6|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||14.1|21.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|17.8|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|20.1|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|21.1|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|23.9|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|24.0|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|24.9|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|26.1|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||15.9|36.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|26.2|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|27.6|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|27.7|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|28.6|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||26.2|30.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|28.8|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|36.3|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|36.4|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|44.0|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|72.5|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|72.8|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Hispanic|102.1|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Multiracial|4.9|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Multiracial|17.4|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|5.8|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|8.8|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|9.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||2.4|16.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|12.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||Native Hawaiian or other PI||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|23.5|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|30.1|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|50.8|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|74.6|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other|102.1|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||54.9|149.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|Other||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|5.4|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|6.2|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|11.0|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|11.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|11.6|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|12.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|12.7|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||9.7|14.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|14.8|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|16.1|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||11.8|20.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|16.9|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||13.4|20.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|18.1|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|19.6|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|21.9|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|22.8|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|26.2|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|26.4|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||23.5|29.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|26.5|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|29.6|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|31.8|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|32.4|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||All races other than black prone to swings in dx rate due to small numbers of new dx||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|32.9|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Both|White|50.0|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|2.4|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|2.8|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|4.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|4.8|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|5.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||3.6|6.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|5.2|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||4.2|6.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|5.5|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|6.8|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|6.8|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|7.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|9.4|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|13.1|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|13.1|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|14.2|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|14.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|15.5|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|16.6|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|18.6|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|19.9|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||15.4|24.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|22.9|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Female|All|60.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|13.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|27.7|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|29.0|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||23.5|34.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|29.1|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|29.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|30.6|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||27.2|34.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|30.6|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|34.0|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|44.1|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|45.3|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|46.6|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|48.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|51.1|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|56.3|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||53.1|59.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|56.7|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|60.9|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|63.0|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|63.6|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|64.0|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|64.2|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||55.9|72.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|78.7|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|129.8|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2011|Male|All|190.6|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|8.8|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||To be consistent with 2007 report, HIV (not AIDS) incidence rates are provided.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|10.3|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|10.4|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|14.6|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|15.3|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|15.3|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|16.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|18.7|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||15.6|21.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|19.7|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|19.9|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|23.1|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||20.2|26.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|24.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|26.0|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|26.4|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|28.7|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|30.6|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|31.4|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||29.7|33.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|31.6|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|31.7|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|32.5|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||28.4|36.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|32.9|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|35.8|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|38.1|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|39.8|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|43.9|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|89.9|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|All|108.7|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|9.9|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|10.2|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native||Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|4.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|4.9|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|6.0|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|6.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||Does not include Pacific Islander||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|7.4|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|7.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|8.3|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|9.2|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|13.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI|13.7|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||10.3|17.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Asian/PI||Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|25.4|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|30.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|34.2|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||18.7|57.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|34.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|34.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|35.7|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|37.2|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|39.6|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|40.0|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|42.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|42.8|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|44.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|45.1|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|50.2|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|51.5|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||42.5|60.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|56.3|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|58.3|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|63.0|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||55.3|70.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|64.8|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|65.5|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||55.5|75.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|69.3|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|71.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|80.2|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|94.1|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|100.1|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|125.1|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Black|158.8|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|7.2|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|10.3|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|11.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|11.8|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|15.1|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|18.5|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|18.5|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|19.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|20.9|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||11.7|30.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|21.2|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|21.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|24.9|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|26.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|27.6|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|28.6|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|29.3|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|31.0|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||28.6|33.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|31.5|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|32.4|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||21.4|47.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|38.9|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|41.0|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|45.3|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|71.3|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|87.0|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic|89.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Multiracial|8.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Multiracial|8.6|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Multiracial|28.6|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|6.8|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|15.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||Native Hawaiian or other PI||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|17.5|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|19.1|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|22.1|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|26.8|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|47.8|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|68.1|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||29.6|106.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other|90.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|Other||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|5.1|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|6.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|6.7|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|7.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|8.5|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.2|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||7.5|12.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.9|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|10.9|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|11.5|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||7.9|15.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|12.3|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|12.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|13.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|16.3|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||13.1|20.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|19.1|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|20.7|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|21.6|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|21.7|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|23.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|23.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|25.3|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||22.4|28.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|25.6|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|25.7|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|28.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|33.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|37.3|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|44.9|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Both|White|67.0|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|2.1|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.1|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.1|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||1.6|5.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|3.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|4.4|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|4.6|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|4.8|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.1|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.1|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.8|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|5.9|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||4.8|6.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|6.0|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|7.2|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|9.2|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|10.3|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||7.1|13.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|11.8|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|13.1|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|13.2|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|14.3|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|16.6|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|18.1|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|19.0|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|26.4|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|48.7|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All|53.2|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|15.3|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|16.7|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|17.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|24.6|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|26.9|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|29.6|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|29.9|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Table 38 (page 2 of 2). Human immunodeficiency virus (HIV) diagnoses, by year of diagnosis and selected characteristics: United States, 2008-2012, http://www.cdc.gov/nchs/hus/contents2014.htm#038.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|34.3|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|34.7|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||28.7|40.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|35.8|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|38.9|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||33.0|44.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|44.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|46.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|47.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|48.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|49.9|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|54.6|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|55.9|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|56.3|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||48.5|64.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|57.2|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||53.9|60.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|59.5|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|60.4|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|68.2|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|70.3|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|102.6|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|137.6|Baltimore, MD|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012|For 2010 data, the denominators are from the 2010 US Census.  For 2011 data, the denominators are from July 1, 2011 U.S. Census Estimates.  For 2012 data, the denominators are from July 1, 2012 U.S. Census Estimates.  We used these denominators to be consistent with the Maryland Dept. of Health and Mental Hygiene.|Adult/Adolescent HIV cases diagnosed >=13 years of age||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2012|Male|All|166.8|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|9.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|9.7|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|9.8|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||To be consistent with 2007 report, HIV (not AIDS) incidence rates are provided.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|13.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|13.6|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||11.0|16.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|15.1|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|15.2|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|19.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|19.5|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|20.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|21.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|22.2|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|22.4|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||19.6|25.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|23.8|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|26.5|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||24.9|28.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|28.1|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|30.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|30.5|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||26.5|34.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|32.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|32.8|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|33.7|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|38.2|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|40.2|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|46.9|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|53.0|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|All|87.4|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|8.0|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2017||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|25.6|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|3.0|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|3.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|5.3|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2018||Asian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|5.6|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|6.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|8.4|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|9.1|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||6.4|11.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|9.8|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|12.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|13.9|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|14.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI|21.9|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.7|37.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|25.4|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|27.4|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|28.4|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|31.9|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||17.0|54.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|32.9|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|34.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|36.6|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|40.0|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|40.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|43.0|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|43.4|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|43.7|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|45.0|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|47.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|49.2|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2019||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|50.3|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||41.5|59.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|57.4|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|58.7|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||49.3|68.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|61.1|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||53.5|68.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|62.6|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|65.3|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|72.5|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|85.8|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|109.0|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|116.5|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Black|126.2|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|5.0|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|10.3|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|11.2|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|12.5|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|14.0|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|14.1|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|17.3|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2020||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|17.6|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||9.9|29.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|19.9|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|20.1|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|21.0|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|21.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|22.6|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|23.0|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||13.4|32.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|23.8|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|24.9|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||22.7|27.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|28.1|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|31.4|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|39.3|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|41.0|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|44.4|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|47.9|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|79.7|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic|82.2|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|7.1|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|12.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|20.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2021||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Multiracial|38.5|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|6.8|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|8.7|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|10.6|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2022||Native Hawaiian or other Pacific Islander||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|12.5|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|17.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|19.4|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|23.4|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|37.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other|41.1|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other||Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|Other||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|5.4|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|5.9|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2021||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|6.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|7.7|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|8.5|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||6.0|11.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|9.1|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|9.2|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|11.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|12.0|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|12.2|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|12.6|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||9.8|15.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|12.7|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||8.9|16.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|13.5|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|14.8|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|17.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|17.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|18.7|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|20.0|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|20.5|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|21.1|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|21.8|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||19.2|24.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|24.5|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|25.2|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|27.6|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|36.7|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|41.1|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Both|White|51.0|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|1.7|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.2|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|2.3|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|3.0|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|3.8|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|4.3|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|4.6|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|5.1|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|5.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|5.5|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|5.9|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||4.9|7.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|6.2|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2016||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|6.5|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|9.1|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||6.4|11.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|11.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|11.8|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|11.9|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||8.4|15.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|11.9|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|12.6|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|12.9|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|13.0|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|19.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|23.3|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Female|All|39.4|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|15.9|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|17.1|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|17.9|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|25.1|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||20.3|30.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|26.4|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|26.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2015||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|27.2|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|33.8|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|34.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|34.4|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|35.4|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS|Excludes those diagnosed with Stage 3 infection (AIDS)|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|36.7|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||31.1|42.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|38.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|40.7|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|DSHS 2013 HIV file for Bexar (annual analysis file) - received in July 2014||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|44.9|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|45.3|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|47.3|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||44.3|50.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|48.1|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|50.4|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||43.0|57.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|53.9|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|56.3|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|56.9|New York City, NY|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|66.3|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|70.3|Chicago, Il|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|84.4|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|87.2|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2013|Male|All|137.9|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|9.1|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|10.7|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||To be consistent with 2007 report, HIV (not AIDS) incidence rates are provided.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|12.0|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|13.8|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2022||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|13.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|15.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|15.6|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||12.8|18.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|17.4|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||15.5|19.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|18.0|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|19.2|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|20.6|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|22.8|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||18.3|28.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|24.4|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||21.4|27.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|24.8|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|28.9|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|31.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|31.8|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||30.1|33.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|35.6|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|37.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|37.9|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|39.9|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||35.3|44.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|53.9|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|All|72.6|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|9.5|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2025||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native||Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. American Indian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|4.3|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|4.7|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|6.2|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2026||Asian alone||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|8.3|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|8.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|9.5|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|11.9|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||8.8|15.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|12.6|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|13.6|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|16.4|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||8.2|29.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|21.5|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||13.7|32.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI|21.9|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.7|37.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|18.2|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|23.4|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|26.5|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|29.2|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||15.1|51.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|33.1|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|33.3|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||23.7|43.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|34.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|36.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|40.1|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|41.3|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||30.0|55.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|48.9|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||40.3|57.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|48.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|49.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2027||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|51.4|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|53.5|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|57.6|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|61.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|62.5|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||54.9|70.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|65.3|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||46.2|89.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|74.8|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|81.0|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||69.8|92.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|81.7|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|102.6|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Black|111.4|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|7.2|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|8.2|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|13.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|17.0|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|17.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||10.0|27.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|17.9|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|18.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|18.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2028||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|18.9|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||16.4|21.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|20.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|21.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|26.1|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||15.9|36.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|26.4|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||16.8|39.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|26.9|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|27.4|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|32.5|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||30.0|35.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|40.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|41.0|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|43.3|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|44.4|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||28.2|66.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|65.7|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic|71.2|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Multiracial|10.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Multiracial|15.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2029||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Multiracial|28.6|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|8.7|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|10.6|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2030||Native Hawaiian or other Pacific Islander||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|11.5|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|18.0|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|18.4|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|22.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||10.7|40.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|32.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|39.7|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||10.3|69.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other|49.4|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other||Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other||Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|Other||San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|4.2|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|6.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2029||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|7.5|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|7.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|9.6|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|9.7|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||7.1|12.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|10.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|12.0|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||9.1|15.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|12.2|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|12.8|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|13.5|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||10.7|17.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|14.6|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||10.4|18.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|15.3|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|15.9|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|16.0|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|16.8|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||9.8|26.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|22.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||18.0|26.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|23.4|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|23.6|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|24.7|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||21.9|27.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|25.9|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|33.9|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|40.3|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Both|White|43.3|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|1.5|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|2.5|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.0|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.1|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||1.6|5.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.5|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|3.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|4.8|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||3.4|6.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|5.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.0|8.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|5.2|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|5.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|5.5|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|5.8|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||4.7|6.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|6.1|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2024||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|8.0|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||4.5|12.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|9.3|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||6.6|12.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|12.2|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|12.7|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||9.1|16.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|13.3|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|13.8|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|14.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|17.0|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|20.6|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Female|All|24.3|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|15.9|Fort Worth (Tarrant County), TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|19.8|San Jose, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|20.1|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|26.0|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|27.4|U.S. Total, U.S. Total|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report 2014. Table 1a. Diagnoses of HIV Infection, by year of diagnosis and selected characteristics, 2010-2023||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|27.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|28.4|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, National Center for Health Statistics Population Estimate (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|confidence intervals calculated using clopper-pearson method|OPHAT, HIV diagnosis query|||23.1|33.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|30.5|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||26.9|34.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|30.9|Long Beach, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|33.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|34.3|Minneapolis, MN|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|New cases of HIV diagnosis (both HIV (non-AIDS) and AIDS at first diagnosis) diagnosed within a given calendar year||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|38.5|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2||Excludes those with a first HIV diagnosis at Stage 3 infection (AIDS)|||30.2|48.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|40.7|Indianapolis (Marion County), IN|HIV cases diagnosed in given year; report crude rate per 100,000 population using 2010 US Census figures.|MCPHD HIV/AIDS Data|||||34.8|46.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|44.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|47.0|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||39.9|55.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|47.1|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|49.5|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|58.0|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||54.7|61.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|59.8|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|62.1|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|69.0|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||60.4|77.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|71.2|San Francisco, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|93.1|Miami (Miami-Dade County), FL|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.7||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2014|Male|All|123.1|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|10.8|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query|||||8.6|13.3
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|11.2|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|15.2|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|15.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|19.2|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||17.2|21.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|21.1|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||17.8|24.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|22.2|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|22.3|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|22.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||20.3|24.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|30.7|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||29.0|32.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|31.2|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|31.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||25.7|36.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|33.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|33.7|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|35.4|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|37.5|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||33.0|41.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|All|65.1|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|8.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|9.7|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|9.9|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|10.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|11.0|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|11.1|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||8.0|14.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|11.9|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||5.1|23.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|13.3|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|19.1|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||12.7|25.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI|19.4|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||12.2|29.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|26.5|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|37.3|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|37.9|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||27.8|48.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|39.4|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|41.7|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|46.5|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||31.0|67.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|48.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|48.5|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|57.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||43.8|73.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|59.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||48.2|69.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|61.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|72.0|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||63.8|80.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|75.0|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|79.7|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|80.6|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||69.5|91.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black|93.3|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|13.4|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query|||||6.9|23.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|13.5|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|14.4|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|18.9|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|19.2|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|21.8|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||19.0|24.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|21.9|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|23.7|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||19.7|27.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|26.3|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|28.3|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||18.8|40.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|28.9|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||26.5|31.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|29.3|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||18.4|40.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|33.5|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|40.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|55.6|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||37.7|79.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Hispanic|100.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|11.0|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||2.9|19.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|13.0|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|14.8|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||6.1|23.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|17.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|18.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||3.7|53.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|19.1|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|30.2|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|43.1|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other|45.4|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||13.9|76.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|Other||Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|6.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|9.3|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||6.8|11.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|9.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|10.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|10.3|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.9|13.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|10.6|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query|||||8.2|13.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|13.1|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|13.4|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|15.2|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||12.7|17.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|16.2|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|16.9|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||13.4|21.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|20.5|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|21.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|21.7|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|21.7|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||13.6|32.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Both|White|23.4|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||20.6|26.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|2.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|3.0|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|3.9|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.2|6.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|4.2|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||2.9|5.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|5.1|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|5.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|6.0|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||5.0|7.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|6.3|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||4.7|7.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|10.9|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||6.9|16.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|11.7|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|12.1|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|12.6|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|13.5|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||9.8|17.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|13.9|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Female|All|28.3|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|19.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|19.7|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|OPHAT HIV diagnosis query|||||15.5|24.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|27.6|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|33.3|Boston, MA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|34.7|San Antonio, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||30.9|38.5
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|38.3|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||34.4|42.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|38.6|Seattle, WA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||32.3|45.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|38.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|48.6|Houston, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|52.8|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||42.4|63.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|55.6|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|55.6|Los Angeles, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.|Include persons who were newly diagnosed with an HIV infection, regardless of the stage of disease.|||52.4|58.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|57.2|Detroit, MI|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|58.8|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|63.1|Charlotte, NC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||54.8|71.4
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2015|Male|All|104.5|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|10.6|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query|||||8.5|13.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|12.2|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|15.2|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|20.2|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|24.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|24.0|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||21.9|26.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|30.7|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||25.2|36.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|31.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|34.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|All|57.7|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|6.9|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|8.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|10.1|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|10.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI|12.4|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||7.2|17.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|30.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|36.0|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|40.8|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|48.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|52.5|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||39.7|68.2
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|55.2|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|70.2|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|84.2|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||71.3|97.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black|84.7|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|14.3|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|20.9|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query|||||12.6|32.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|21.1|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|25.7|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||21.5|29.8
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|28.3|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|28.4|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|34.3|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||23.8|48.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|49.0|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Hispanic|65.8|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Other|11.5|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Other|15.5|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Other|21.9|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Other|24.2|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||6.6|62.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|7.7|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|10.9|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|11.4|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|12.7|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|13.6|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||11.2|15.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|20.3|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|21.1|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|22.7|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||14.4|34.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Both|White|22.9|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|3.4|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|3.6|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|5.9|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|6.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||5.3|8.6
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|9.4|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||5.7|14.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|9.6|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|14.5|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All|27.1|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|20.2|Portland (Multnomah County), OR|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|OPHAT HIV diagnosis query|||||16.0|25.1
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|20.9|Phoenix, AZ|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|26.8|San Diego County, CA|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017.; Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|35.4|Kansas City, MO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|40.9|Las Vegas (Clark County), NV|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data||||||36.9|44.9
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|43.7|Denver, CO|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|50.4|Philadelphia, PA|HIV cases diagnosed in 2012, 2013, 2014 (as available); report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|AACO team||||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|53.3|Oakland (Alameda County), CA|HIV cases diagnosed in a given year; report crude rate per 100,000 pop using 2010 US Census figures. If 2012-2014 not available, provide three most recent years of data|Alameda County eHARS, Q2 2017|||||42.9|63.7
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|59.8|Dallas, TX|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|HIV Diagnoses Rate (Per 100,000 people)|2016|Male|All|89.4|Washington, DC|HIV cases diagnosed in a given year; report crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.1|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.6|2.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.6|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.6|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.7|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.9|3.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.1|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.4|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||2.5|4.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.5|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.5|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||2.2|5.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.7|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.9|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates||||2.2|6.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.1|Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.1|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.6|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.2|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|7.2|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|7.3|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||6.3|8.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.4|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.8|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|20.4|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|0.0|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|1.6|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||American Indian alone||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.0|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.7|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|1.1|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||0.6|2.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|1.5|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.0|3.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|8.8|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|0.9|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|7.2|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||3.4|10.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.8|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates||||4.4|15.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|9.3|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|10.3|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|11.8|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|11.8|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.3|28.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|12.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|12.1|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|12.6|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|15.5|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|16.1|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|20.8|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|22.3|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||15.7|31.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|36.4|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|43.0|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|0.0|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|1.6|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.5|2.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.6|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.8|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|3.1|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|3.3|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|3.5|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||2.4|4.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|3.6|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|3.8|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.8|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.2|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|8.4|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||5.6|12.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|6.1|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|0.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|0.0|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|2.1|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.0|6.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|5.2|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.1|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.8|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.9|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.3|2.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.0|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.1|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.3|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.4|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.4|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.5|3.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.0|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.4|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||1.9|6.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.7|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.9|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|5.8|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.3|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||7.6|11.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|0.0|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|0.0|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|0.7|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|0.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.2|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.5|1.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.4|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.5|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.6|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||1.0|2.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.7|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.7|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.2|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.0|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|17.5|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|2.6|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|3.8|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|4.0|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||3.0|5.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|4.1|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.8|5.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|4.2|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|5.5|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|5.5|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||4.0|7.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|6.1|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2010 mid-year population estimates||||3.2|10.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|6.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|6.9|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|6.9|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.3|11.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|8.5|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|8.7|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.1|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|12.6|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010; Table IX, United States Standard Population, National Vital Statistics Reports Volume 59 Number 10, 2011.||Value is reported for a multi-year period, 2010-2012|||10.8|14.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.4|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|14.1|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|23.9|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|1.9|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.4|2.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.4|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.6|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.7|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.8|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.1|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.4|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.6|4.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.8|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|eHARS|Deaths are of PLWHA residing in Oakland at the time of death and for whom an HIV-related ICD code was listed anywhere on the death certificate (i.e., HIV may not have been the immediate cause of death); estimates are not age-adjusted due to small numbers; rates based on counts < 10 are not shown due to statistical instability|Death data incomplete for 2013 (and possibly, to a lesser extent, for 2012 as well)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.9|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.7|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.0|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.5|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.6|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.3|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.7|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|1.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||American Indian alone||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|31.0|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|1.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.0|2.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|1.8|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|1.8|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|7.6|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.3|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.4|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.6|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.7|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.2|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.3|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.7|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.8|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|11.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|13.2|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|13.2|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|14.9|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||9.3|20.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|20.6|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|23.6|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|27.1|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|35.9|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|1.8|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.3|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.9|3.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.3|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.7|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.9|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.1|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.1|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.8|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.4|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|6.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|9.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|10.4|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|26.0|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|0.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|1.1|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|0.7|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|0.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.5|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.7|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.8|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.2|2.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.1|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.1|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.1|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.3|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.3|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.5|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.5|3.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.0|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.2|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.5|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.6|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|21.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|0.8|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.3|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.4|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.4|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.5|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.6|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.8|2.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.9|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.9|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.3|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.7|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.8|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.0|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.8|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.3|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|9.2|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|3.4|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|3.6|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||2.7|4.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.2|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.5|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.6|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.2|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||3.8|6.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.5|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.2|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|7.1|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|7.4|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|7.9|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|eHARS|Deaths are of PLWHA residing in Oakland at the time of death and for whom an HIV-related ICD code was listed anywhere on the death certificate (i.e., HIV may not have been the immediate cause of death); estimates are not age-adjusted due to small numbers; rates based on counts < 10 are not shown due to statistical instability|Death data incomplete for 2013 (and possibly, to a lesser extent, for 2012 as well)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.2|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.7|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.4|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|12.9|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|15.5|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|21.0|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|26.4|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|1.8|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.3|2.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|1.9|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.2|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.3|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.5|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.8|Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|CDC Wonder: B20-B24|||||1.8|4.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|2.9|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.0|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.3|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.8|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||3.0|4.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.4|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.7|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.9|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.8|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|7.9|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.4|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates||||5.7|14.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.7|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|15.8|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.0|Baltimore, MD|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|0.0|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|1.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||American Indian alone||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|0.0|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|0.7|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.1|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.0|4.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|0.9|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1.6|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|4.8|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|4.9|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|5.0|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.5|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.8|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.6|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.7|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.4|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates||||4.7|16.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.8|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|11.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||6.3|15.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|11.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|12.4|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|16.1|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|17.5|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|27.2|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|34.3|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|1.8|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.2|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.8|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.8|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.3|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.6|5.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.7|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.9|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.7|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|11.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|0.0|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|1.5|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|13.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|0.7|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.0|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.5|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.0|2.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.8|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.9|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.2|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.2|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.4|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.5|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.1|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.3|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.2|4.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.8|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.2|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|19.5|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|0.4|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|0.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.0|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.1|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.3|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.6|2.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.0|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.0|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.1|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.8|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.3|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.4|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.8|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.6|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.9|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|9.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1.0|Cleveland, OH|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|2.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|3.1|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||2.3|4.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|3.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|3.7|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|3.9|Los Angeles, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|4.7|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.2|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.5|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.2|Houston, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.4|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||4.8|7.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|7.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|7.4|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.4|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2011 mid-year population estimates||||4.9|13.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.8|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.5|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|13.1|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|20.3|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|22.6|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|23.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|1.8|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.3|2.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.1|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.5|Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|CDC Wonder: B20-B24|||||1.6|3.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.5|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.8|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||2.1|3.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.9|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.3|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.5|4.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.8|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.2|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.7|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates||||2.9|7.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.4|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.6|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||5.6|7.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.7|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.0|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.7|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.0|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.7|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|1.3|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|19.7|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|40.3|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|American Indian alone||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.8|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||0.3|1.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|1.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.0|2.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Age-adjusted rates based on small numbers are unreliable and have been suppressed.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|3.3|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.1|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.2|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.5|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates||||4.1|15.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|9.0|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||5.8|13.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|9.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|9.3|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|12.8|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.3|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.8|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|15.9|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||10.9|23.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|22.0|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|31.7|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|33.0|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.3|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.1|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.6|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.2|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.5|4.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.2|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.3|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.5|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|10.6|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||7.3|15.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|10.7|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|0.0|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|0.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|4.4|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|0.9|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.1|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.4|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.1|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||1.3|3.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.1|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.5|2.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.2|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.4|3.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.3|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.3|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.4|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.4|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.4|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.5|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.6|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|4.5|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.3|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24||2011-2013 years are the most recently available data at this time.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.3|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||6.7|10.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|20.3|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|0.6|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.1|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.1|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.4|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.6|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||1.0|2.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.9|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.0|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.7|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.9|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.5|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.7|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|9.1|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|2.9|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||2.1|3.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|3.1|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|4.0|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|HIV-Related Mortality Rate ICD-10 Codes: B20-B2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|4.7|Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|CDC Wonder: B20-B24|||||2.8|7.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|4.9|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||3.6|6.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.5|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||4.0|6.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.8|Chicago, Il|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.2|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2012 mid-year population estimates||||4.0|11.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|8.9|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|9.5|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|10.9|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.0|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.0|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|2011, 2012, 2013; per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.4|San Francisco, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||Value is reported for a multi-year period, 2013-2015|||9.7|13.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|13.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|15.6|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|28.3|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.6|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.6|4.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.7|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.0|3.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.9|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||2.2|3.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.1|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.3|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.5|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County Vital Statistics|||||1.9|5.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.5|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||2.7|4.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.1|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.7|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.7|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.3|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|11.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|11.2|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|1.2|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|15.2|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|0.3|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|2.8|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|8.5|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B25|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Age-adjusted rates based on small numbers are unreliable and have been suppressed.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|2.4|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|2.7|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|6.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.6|9.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|6.9|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||4.3|10.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.1|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.5|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County Vital Statistics|||||3.6|13.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|8.2|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|8.6|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.4|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|13.0|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|14.0|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|19.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|19.6|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|28.8|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|0.0|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|0.0|Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.3|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.0|3.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.6|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|3.6|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||2.5|5.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|4.2|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.5|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.9|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|6.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.3|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|15.9|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B25|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|0.0|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|8.3|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B25|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|0.0|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||Crude rates are displayed for races as census does not give race by age population totals for Detroit.  Should not be an issue as other group (sex and total) rates were only increased by 0.1 when standard population weights were included.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|0.9|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.1|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||0.7|1.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.4|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.9|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.9|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.0|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.5|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.6|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||1.7|3.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.8|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.5|5.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.0|4.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.5|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.4|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|0.4|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|0.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.1|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.7|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.0|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.1|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.4|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.8|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.6|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B25|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|2.6|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|ehars||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|2.6|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.9|3.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|3.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|HIV infection deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|4.3|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||3.6|6.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.1|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||3.7|6.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.1|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.0|8.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.8|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||4.3|7.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|6.1|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|6.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|6.7|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County Vital Statistics|estimates are not age-adjusted due to small numbers||||3.7|11.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.5|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|9.1|Miami (Miami-Dade County), FL|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|11.4|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.6|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.4|Long Beach, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|1.5|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.2|2.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|1.7|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|1.9|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||1.4|2.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.3|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||1.7|3.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.4|Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24|||||1.5|3.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.6|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.5|4.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.0|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.0|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||2.3|3.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.4|Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.5|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.7|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.9|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|4.1|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates||||2.4|6.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|5.2|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|5.2|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|10.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|1.4|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|28.2|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|0.4|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|0.7|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|1.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.0|2.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B26|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Age-adjusted rates based on small numbers are unreliable and have been suppressed.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1.6|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|3.5|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|5.8|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|5.9|Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|6.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|6.7|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|7.1|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates||||3.4|13.1
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|7.8|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||3.8|11.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|7.9|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|8.4|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||5.4|12.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|11.6|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.7|25.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|11.8|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|13.2|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|18.5|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|0.7|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.1|1.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|1.8|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.8|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.9|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||2.0|4.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|4.7|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|5.5|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|5.6|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|6.8|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B26|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|2.2|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|6.5|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|29.9|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B26|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|0.9|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.1|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.5|1.7
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.2|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.3|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.3|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||0.8|1.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.6|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.7|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.8|4.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.8|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.8|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|2.9|Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.5|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|0.0|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|0.3|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.0|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||0.3|1.6
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.0|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.2|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.1|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.7|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.1|New York City, NY|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|NYC DOHMH Bureau of Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.9|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B26|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. For this indicator, the number is too small for rate calculation.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|2.8|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||2.1|3.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|2.8|U.S. Total, U.S. Total|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|3.1|Las Vegas (Clark County), NV|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Nevada Vital Records - Clark County Deaths||All ICD-10 fields included|||2.0|4.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|3.7|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|3.8|Fort Worth (Tarrant County), TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|Includes all deaths which list HIV as a contributing cause of death (ICD10 Codes: B20-B24)|||2.6|5.2
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.4|Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder B20-B24|||||2.7|6.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.9|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.9|Seattle, WA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.8|8.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|5.3|San Antonio, TX|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||3.9|7.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|5.6|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|5.8|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|6.8|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|7.1|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2015 mid-year population estimates||||4.0|11.5
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|7.4|Boston, MA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|8.7|Detroit, MI|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|15.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|1.6|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||1.2|2.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|2.4|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|2.5|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|2.9|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|3.4|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|3.8|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|4.1|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates||||2.4|6.3
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|12.3|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|0.0|Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|1.4|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|2.6|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|4.1|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|5.1|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|9.4|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates||||4.9|16.4
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|13.0|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|22.2|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|2.1|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|5.5|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|6.2|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|0.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|5.2|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|1.1|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|1.3|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||0.9|1.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|2.6|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|2.8|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|3.1|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|0.0|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|0.8|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|0.9|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|1.2|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.5|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|7.5|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|3.0|San Diego County, CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jan 17, 2018 6:50:08 PM|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates. Non-stated ethnicities were not included in the analysis.||||2.2|3.9
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|4.2|Kansas City, MO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|4.6|Minneapolis, MN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Minnesota Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.2|Denver, CO|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Mortality data from the Colorado Department of Public Health and Environment||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.5|Philadelphia, PA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|Vital Statistics||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.8|Phoenix, AZ|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Arizona Department of Health Services (ADHS) HIV Epidemiology Dpt||Maricopa County level data||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|7.3|Oakland (Alameda County), CA|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|Alameda County vital statistics files|Using 2016 mid-year population estimates||||4.2|11.8
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|18.0|Washington, DC|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|National Center for Health Statistics - Underlying Cause of Death Files on CDC WONDER Online Database, released December, 2017.||||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All||Indianapolis (Marion County), IN|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2010 standard population.  ICD-10 Codes: B20-B24|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
HIV/AIDS|HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All||Portland (Multnomah County), OR|HIV-related deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 Codes: B20-B24|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|49.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|207.1|Fort Worth (Tarrant County), TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas HIV Surviellance Report, 2010, Texas Department of State Health Services, HIV/STD Program|||||200.4|213.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|256.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|263.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||255.7|271.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|276.7|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|362.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||354.0|370.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|383.5|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|386.2|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||372.0|400.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|495.6|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|696.0|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||687.9|704.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|762.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||735.2|789.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|778.8|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|849.5|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|877.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|917.6|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|990.0|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|All|2495.7|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|American Indian/Alaska Native|118.2|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||American Indian alone; May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|9.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|21.9|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||6.7|37.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|64.8|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||Just Asian, not Pacific Islander||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|65.1|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|88.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|94.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|109.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||94.4|125.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|138.2|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|151.3|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||140.1|162.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|159.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||129.1|189.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|173.7|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|174.5|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Asian/PI|205.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|76.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|551.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||509.9|592.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|686.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|777.4|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||742.9|811.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|937.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||894.8|980.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|966.5|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1034.1|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1227.1|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1308.9|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1363.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1413.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1437.3|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1400.8|1473.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1506.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||1432.9|1579.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|1680.2|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|2459.1|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Black|3449.0|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|68.8|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|177.7|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||151.0|204.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|202.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|263.8|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||253.8|273.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|265.7|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||252.3|279.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|289.5|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|327.1|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||Hispanic of any race, not Hispanic as race||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|376.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|422.9|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||382.5|463.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|481.5|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|511.6|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|552.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||541.9|562.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|562.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|1232.3|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Hispanic|1303.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Multiracial|32.2|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|74.1|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||50.6|97.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|118.5|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||93.8|143.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|211.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|280.6|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|443.3|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||Multiple races||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|470.7|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||369.4|572.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|496.9|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||395.2|616.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|569.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|852.1|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|Other|1432.1|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|19.2|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|140.9|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|183.6|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||169.0|198.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|187.1|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||175.5|198.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|257.7|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|299.7|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|368.7|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||356.4|381.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|403.4|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|618.3|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|742.9|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|755.1|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||701.8|808.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|814.3|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||798.3|830.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|822.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|859.8|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|989.5|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Both|White|1070.6|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|25.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|69.0|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|76.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|81.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||75.3|87.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|122.3|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||115.3|129.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|137.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|140.0|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|142.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||137.4|147.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|161.2|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|215.7|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||200.9|230.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|222.7|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|232.7|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|286.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||263.4|310.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|316.3|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|516.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Female|All|1729.1|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|76.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|447.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|452.4|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||438.1|466.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|510.8|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 24, 2012. 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2008-2011 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|568.6|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||543.7|593.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|599.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||584.1|614.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|662.2|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|688.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1254.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1238.8|1269.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1267.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||1217.1|1317.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1269.5|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1343.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1559.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1614.5|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|1803.5|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2010|Male|All|3380.5|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|227.0|Fort Worth (Tarrant County), TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas HIV Surviellance Report, 2010, Texas Department of State Health Services, HIV/STD Program|||||220.0|233.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|243.8|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|268.4|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|282.6|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|369.3|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||360.8|377.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|384.2|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||370.3|398.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|392.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|427.0|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||412.0|442.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|488.7|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|516.3|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|566.8|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|718.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||710.3|726.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|725.2|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|800.2|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|820.5|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|865.5|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|903.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|951.3|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|1019.8|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|1039.3|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|1245.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|1383.1|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|2318.1|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|All|2590.2|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|122.2|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|311.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|411.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|496.6|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method|American Indian alone|||330.3|717.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|1305.3|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|30.1|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||12.3|47.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|68.0|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||Does not include Pacific Islander||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|75.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|97.0|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|100.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|106.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|124.4|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||96.4|157.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|128.9|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||112.2|145.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|135.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|145.1|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|150.8|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|159.1|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||147.6|170.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|161.4|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|177.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|180.8|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|220.4|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI|229.4|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|422.8|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|615.8|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||541.9|697.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|720.9|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|739.2|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|780.6|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|855.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||819.4|891.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|966.0|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||922.4|1009.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|990.5|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1037.9|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1056.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1258.4|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1364.1|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1395.8|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1479.7|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1483.1|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1446.0|1520.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1568.3|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1630.7|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|1859.6|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|2248.1|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|2544.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|2688.7|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|3171.8|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Black|3889.7|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|203.8|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||175.2|232.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|211.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|261.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|278.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||264.5|291.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|306.2|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|307.9|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|330.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|332.0|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|350.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||311.0|393.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|365.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|389.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|443.1|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|502.9|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|532.0|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|575.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||564.7|585.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|594.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|879.2|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|1246.5|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|1519.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|1562.2|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|1703.4|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Hispanic|2012.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Multiracial|81.0|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Multiracial|232.6|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||178.4|298.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Multiracial|390.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Multiracial|492.9|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Multiracial|572.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Multiracial|1585.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|127.9|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||102.2|153.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|159.4|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||Native Hawaiian or other PI||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|207.0|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|235.3|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|247.5|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|307.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|384.6|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|447.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|572.8|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||461.1|684.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|604.4|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|901.4|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other|990.0|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|Other||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|146.2|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|165.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|200.3|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||185.0|215.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|267.7|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|298.4|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|365.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||352.9|377.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|404.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||387.5|421.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|407.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|470.3|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|555.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|617.7|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|625.8|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|728.1|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|731.6|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|738.6|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|791.7|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|828.5|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||812.3|844.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|838.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|854.7|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|1008.9|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|1054.3|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|1100.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Both|White|1232.3|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|67.9|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||59.9|76.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|73.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|76.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|78.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|122.6|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|123.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||116.1|130.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|144.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|144.9|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||139.7|150.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|163.7|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|188.9|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|221.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|232.9|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|235.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||220.0|251.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|305.7|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|320.8|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|341.2|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|375.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|397.7|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|533.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|691.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|749.4|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|1335.6|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Female|All|1624.0|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|418.0|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|467.6|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|523.7|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Table 14a. Persons living with diagnosed HIV infection, by year and selected characteristics, 20092012United States, http://www.cdc.gov/hiv/library/reports/surveillance/2013/surveillance_Report_vol_25.html||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|612.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||596.7|627.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|631.7|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||605.5|657.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|692.4|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|705.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|708.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||681.5|735.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|869.7|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|919.8|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1114.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1297.1|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1281.5|1312.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1308.9|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1367.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1374.3|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1392.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1663.5|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1854.1|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1869.7|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|1873.1|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|2080.5|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|3118.8|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2011|Male|All|3990.8|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|231.5|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|245.7|Fort Worth (Tarrant County), TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas HIV Surviellance Report, 2010, Texas Department of State Health Services, HIV/STD Program|||||238.4|252.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|258.5|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|276.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|289.0|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|391.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||377.6|405.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|400.5|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|415.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|447.2|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||433.8|461.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|472.4|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||456.6|488.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|507.5|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|525.9|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|625.0|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|744.2|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|744.9|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||736.5|753.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|769.3|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|818.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|826.5|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|879.3|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|933.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|980.9|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|1044.6|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|1065.2|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|1255.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|1391.0|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|1881.8|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|2541.1|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|All|2651.4|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|119.0|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||American Indian alone; May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|266.1|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|374.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|485.9|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method|American Indian alone|||323.1|701.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|494.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|1039.2|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|1289.6|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|38.3|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||18.2|58.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|65.2|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|68.2|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||Just Asian, not Pacific Islander||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|79.3|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|104.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|107.4|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|134.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||105.7|168.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|142.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|150.6|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|153.0|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|165.3|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|166.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|171.9|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||160.0|183.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|183.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|184.3|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|216.5|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|233.4|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|235.1|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI|449.2|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||359.4|539.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|449.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|633.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||558.7|715.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|755.9|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|768.5|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||733.9|803.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|788.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|802.8|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|897.9|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|953.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||915.4|991.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|997.9|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1067.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1076.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1156.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1296.4|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1417.1|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1419.2|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1448.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1502.0|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1543.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1505.3|1581.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1585.2|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1629.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|1872.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|2319.0|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|2608.0|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|2720.6|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|3395.0|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|3961.8|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Black|4104.2|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|223.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|226.8|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||196.6|257.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|276.8|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|284.4|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|321.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|325.3|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|339.0|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|345.8|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||Hispanic of any race, not Hispanic as race||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|350.4|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||311.2|389.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|354.7|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|372.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||332.2|416.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|385.3|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|399.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|450.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|526.9|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|560.8|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|602.7|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||591.9|613.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|613.4|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|618.9|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|899.5|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|1476.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|1533.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|1564.4|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|1631.3|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|1784.1|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Hispanic|2206.5|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|87.2|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|92.9|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|99.6|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|221.9|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||169.8|285.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|346.0|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|355.3|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|594.7|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Multiracial|1596.2|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|107.6|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|238.4|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|262.5|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|310.7|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|384.6|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|405.7|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|447.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|533.6|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||Multiple races||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|545.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|615.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|629.5|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||526.3|732.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|657.9|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||538.1|777.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|890.6|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other|1056.0|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|Other||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|147.1|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|175.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|213.9|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||198.1|229.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|270.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|292.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|303.3|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|308.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|308.6|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||293.8|323.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|410.2|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||393.4|427.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|415.4|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|562.9|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|622.6|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|643.5|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|660.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|673.3|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|744.3|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|746.0|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|799.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|823.3|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|844.5|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||828.1|860.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|867.9|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|967.0|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|1021.6|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|1082.3|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|1127.4|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|1272.0|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Both|White|2765.4|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|60.0|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|67.7|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||59.7|76.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|75.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|78.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|79.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|125.6|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|132.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|149.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||143.9|154.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|150.0|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|166.0|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|169.9|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||158.2|181.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|192.4|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|223.8|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|235.2|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|235.6|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|239.2|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|255.9|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||239.8|272.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|304.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|327.6|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|342.6|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|383.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|398.3|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|543.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|690.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|747.4|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|1358.4|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Female|All|1750.5|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|401.1|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|445.9|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|481.0|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|535.7|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|694.3|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|703.8|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||676.1|731.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|710.1|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|719.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|723.4|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||696.5|751.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|745.4|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||720.0|770.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|905.0|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1023.6|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1146.1|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1296.6|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1345.7|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1329.8|1361.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1355.9|Chicago, Il|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1364.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1402.8|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1443.9|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1716.5|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1888.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1898.4|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|1919.4|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|2097.5|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|3454.1|Baltimore, MD|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Annual HIV Epidemiological profiles 2010, 2011, 2012||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|3492.4|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2012|Male|All|4094.6|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|222.8|Fort Worth (Tarrant County), TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas HIV Surviellance Report, 2010, Texas Department of State Health Services, HIV/STD Program|||||216.0|229.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|238.7|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|275.7|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|285.0|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|295.1|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|395.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||381.3|409.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|406.5|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|423.0|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|468.6|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||454.9|482.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|520.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||504.0|537.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|520.9|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|547.0|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|650.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|770.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|770.8|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||762.3|779.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|778.4|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|825.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|893.8|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|959.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|1011.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|1066.1|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|1067.2|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|1282.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|1399.2|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|1903.4|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|All|2714.0|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|123.2|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||American Indian alone; May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|201.0|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|266.1|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|374.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|481.8|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method|American Indian alone|||320.4|695.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|494.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|1039.2|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|1292.9|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|60.2|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||35.0|85.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|70.5|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|70.9|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||Just Asian, not Pacific Islander||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|90.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|94.7|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|108.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|111.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|135.6|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||107.2|169.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|153.0|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|168.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|177.3|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|183.0|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||170.7|195.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|186.3|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|189.2|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|192.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|244.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|247.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI|441.6|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||356.4|526.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|484.1|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|645.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||570.2|728.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|769.3|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|816.1|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||780.7|851.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|832.5|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|845.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|912.5|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|984.0|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1018.1|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1053.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||1013.5|1093.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1080.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1093.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1221.3|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1427.7|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1457.5|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1512.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1562.5|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1595.8|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1557.2|1634.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1613.1|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1642.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|1924.9|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|2295.2|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|2676.3|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|2742.9|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|4045.8|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Black|4125.6|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|235.2|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|249.8|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||218.1|281.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|295.6|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|296.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|332.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|343.3|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|345.1|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|350.8|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||Hispanic of any race, not Hispanic as race||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|374.8|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||334.8|414.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|375.6|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|378.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||338.4|42.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|403.4|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|408.8|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|454.3|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|583.4|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|629.0|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||618.0|640.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|637.6|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|646.3|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|916.4|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|918.9|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|1525.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|1548.9|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|1567.2|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|1862.9|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Hispanic|2264.0|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|85.2|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|104.6|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|126.7|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program|PLWHA. Crude rate per 100,000 population.  Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|217.4|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||166.4|279.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|333.9|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|438.3|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|597.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Multiracial|1660.6|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|109.6|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|111.4|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|239.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|265.9|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|318.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|372.2|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|418.7|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|447.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|523.5|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||Multiple races||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|545.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|626.7|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||525.8|727.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|638.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|725.9|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||600.2|851.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|930.6|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other|1093.3|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|Other||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|150.3|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||May include those of Hispanic origin||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|184.9|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|231.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||215.1|248.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|274.2|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|295.1|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|308.8|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|312.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|317.5|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||302.5|332.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|414.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||397.6|431.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|419.8|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|584.5|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|600.1|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|620.8|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|645.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|675.0|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|753.9|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|759.7|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|831.1|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|835.0|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|864.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||847.6|880.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|880.7|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|1035.5|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|1082.9|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|1149.8|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|1304.0|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Both|White|2781.7|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|60.8|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|68.1|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||60.1|76.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|78.2|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|80.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|81.1|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|127.7|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|134.0|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|152.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||147.2|157.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|154.6|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|167.7|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|179.4|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||167.4|191.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|191.7|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|223.6|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|237.7|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|244.7|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|248.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|278.6|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||261.8|295.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|301.7|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|351.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|392.2|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|404.4|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|406.4|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|555.5|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|693.9|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|745.5|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Female|All|4199.6|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|414.6|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|478.7|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|496.2|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|547.4|U.S. Total, U.S. Total|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV Surveillance Report Vol 26, 2014. 18a. Persons living with diagnosed HIV infection, by year and selected characteristics, 2010-2013 - United States||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|708.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|729.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|730.4|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||703.5|758.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|742.6|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|779.0|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||750.0|808.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|779.7|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||753.8|805.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|930.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1061.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1192.3|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1301.8|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1312.7|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1381.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1383.2|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1394.3|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1378.1|1410.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1426.9|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1495.1|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1763.8|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1923.1|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/STD Program, Public Health - Seattle & King County|People living with HIV/AIDS; crude rate per 100,000 pop using annual WA State Office of Financial Management population estimates.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1937.0|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|1940.8|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|2115.7|New York City, NY|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH, HIV Epidemiology and Field Services Program||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2013|Male|All|3535.2|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|248.1|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|265.7|Fort Worth (Tarrant County), TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas HIV Surviellance Report, 2010, Texas Department of State Health Services, HIV/STD Program|||||258.2|273.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|296.7|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||288.9|304.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|302.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|400.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||386.6|414.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|414.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|432.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|488.9|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||475.0|503.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|533.7|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|556.2|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|567.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||550.3|584.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|713.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||693.0|734.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|791.0|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||782.3|799.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|799.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|800.9|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|842.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||814.1|871.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|849.0|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|909.5|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|985.9|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|1052.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|1088.3|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|1277.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|All|1919.4|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|201.0|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|310.4|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|395.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|497.8|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method|American Indian alone|||333.1|714.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|944.7|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|American Indian alone||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|74.1|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|79.3|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||50.5|108.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|100.0|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|108.3|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|114.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|124.4|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|138.8|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||110.6|172.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|162.3|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|175.9|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|182.0|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|189.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|194.7|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||182.0|207.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|205.3|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||171.1|239.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|254.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|314.9|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||281.0|352.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI|452.7|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||369.3|536.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|625.1|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||583.4|666.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|661.1|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||585.1|744.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|786.0|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|851.4|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||815.4|887.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|864.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|930.6|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|935.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1003.4|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1106.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1111.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1152.4|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||1110.4|1194.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1279.5|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1187.0|1377.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1462.0|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1498.0|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1563.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1579.0|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1623.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1584.8|1662.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1634.5|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||1558.4|1710.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1655.6|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|1925.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|2783.9|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Black|4104.2|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|255.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|278.0|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||244.6|311.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|301.1|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||290.9|311.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|311.8|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|345.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|351.3|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|359.2|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|394.2|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||353.6|438.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|396.2|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||355.4|436.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|418.8|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|419.8|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|484.5|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||441.3|527.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|602.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|655.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||644.4|666.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|668.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|686.2|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|931.0|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|936.0|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|1042.3|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||954.0|1137.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|1538.3|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Hispanic|2307.6|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Multiracial|85.2|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Multiracial|112.7|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Multiracial|208.0|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||158.8|267.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Multiracial|661.3|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Multiracial|1492.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|68.9|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||48.0|89.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|235.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|235.1|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|339.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|406.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||314.6|515.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|432.8|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|447.2|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|554.1|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|583.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|636.9|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||536.3|737.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|667.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|709.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||632.0|794.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|777.0|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||646.9|907.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other|906.0|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|Other||Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|199.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||187.6|211.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|247.3|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||230.3|264.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|285.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|294.0|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|315.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|316.5|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|329.6|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||314.3|344.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|419.3|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||402.4|436.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|428.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|603.2|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|607.9|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|632.3|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|652.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||629.0|677.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|682.9|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|764.9|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|767.6|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|853.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||797.2|910.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|873.1|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||856.5|889.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|882.8|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|1049.7|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|1177.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Both|White|2794.8|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|62.1|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|69.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||61.5|78.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|82.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|84.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|89.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||83.2|95.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|109.1|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||98.0|121.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|131.9|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|136.9|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|155.9|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||150.5|161.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|158.6|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|187.0|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||174.8|199.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|222.3|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|249.4|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|251.9|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|299.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||282.1|317.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|300.7|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||276.8|324.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|352.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|401.5|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|409.5|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|409.8|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|566.7|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Female|All|687.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|432.0|San Jose, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, ehanced HIV/AIDS reporting system (eHARS), data as of April, 2015; U.S. Census Bureau, Census 2010||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|510.7|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||496.1|525.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|525.4|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|724.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|739.0|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Oregon Public Health Epidemiologists' User System|confidence intervals calculated using clopper-pearson method||||712.2|766.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|743.3|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|760.5|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|814.1|Indianapolis (Marion County), IN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures. I|MCPHD HIV/AIDS Data|||||787.7|840.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|854.0|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||823.5|884.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|951.8|Long Beach, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Source: U.S. Census Bureau, 2010 Census; California Department of Public Health Office of AIDS data reported as of March 29, 2016; prepared by City of Long Beach Department of Health and Human Services HIV/STD Surveillance Program, 2016.||Counts from which rates are derived are subject to change due to late reporting.  Data represent individuals who were diagnosed in Long Beach but who may not necessarily reside in Long Beach.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1243.2|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|ehars||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1315.9|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1277.0|1356.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1331.9|Cleveland, OH|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1343.4|Minneapolis, MN|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, HIV/AIDS Surveillance System|2010 US Census|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1418.3|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Alameda County eHARS, 2016 Q2|||||1365.1|1471.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1431.5|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1415.0|1448.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1454.5|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1567.2|Miami (Miami-Dade County), FL|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|eHARS V4.8||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1813.1|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1938.3|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|1977.7|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2014|Male|All|3568.0|San Francisco, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|1. San Francisco HIV surveillance data reported as of Jun. 19th, 2015.2. Census Data: San Francisco County Demographic Profile Data, U.S. Census Bureau, 2010.||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|278.5|Fort Worth (Tarrant County), TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas HIV Surviellance Report, 2010, Texas Department of State Health Services, HIV/STD Program|||||270.8|286.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|305.0|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||297.2|312.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|311.9|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|400.0|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||386.2|414.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|422.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|448.0|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||438.6|457.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|580.9|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|591.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|612.1|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||594.2|630.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|692.0|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|797.2|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||788.6|805.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|800.6|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|837.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||808.8|866.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|915.4|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|1107.7|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|1109.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|All|1263.4|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|82.1|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||52.7|111.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|108.3|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|114.6|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|119.7|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|119.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|134.8|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|189.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||169.5|210.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|193.7|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|195.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||182.8|208.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|208.2|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|208.3|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||173.8|242.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI|274.2|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|638.6|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||597.1|680.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|638.8|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1009.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1125.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1227.3|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||1178.1|1276.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1249.6|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||1205.9|1293.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1349.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1529.6|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1584.7|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1604.5|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||1529.1|1679.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1647.4|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1608.2|1686.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1656.5|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1672.4|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1728.1|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Black|1902.7|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|250.6|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|273.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|307.3|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||272.1|342.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|311.7|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||301.4|322.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|368.6|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||352.8|384.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|378.7|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|415.3|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|430.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|503.7|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||459.6|547.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|670.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||659.2|682.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|690.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|944.0|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|1355.2|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Hispanic|1551.6|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Multiracial|1596.2|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|72.5|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||51.5|93.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|207.4|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||139.9|295.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|240.5|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|249.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||213.2|285.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|364.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|509.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||406.0|630.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|603.8|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|607.9|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|702.2|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|845.0|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||709.3|980.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|913.7|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|Other|1205.3|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|202.6|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||190.8|214.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|259.5|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||242.1|276.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|283.7|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|298.4|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|374.1|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|398.7|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||385.9|411.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|434.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|609.7|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|618.2|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|761.9|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|771.2|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|827.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||771.4|883.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|858.6|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||842.1|875.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|1064.4|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Both|White|1327.1|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|84.0|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|87.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|90.2|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||84.2|96.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|123.8|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|144.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||136.5|151.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|158.4|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||153.0|163.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|182.3|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|254.9|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|262.1|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|283.4|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|287.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||264.4|311.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|318.9|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||300.9|336.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|367.6|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|414.5|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Female|All|679.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|526.3|San Antonio, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Texas DSHS, Texas Health Data Center for Health Statistics ||Bexar County level data|||511.6|541.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|541.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|747.8|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||730.7|764.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|756.8|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|794.8|Houston, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|925.6|Charlotte, NC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Population Estimate: US Census 2010 Population for City of Charlotte, NC.  HIV/AID Data: NC DHHS HIV/STD Prevention and Care Unit, Mecklenburg County Line List Data|||||893.9|957.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|935.5|Detroit, MI|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|1131.4|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|1421.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||1367.7|1474.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|1441.5|Los Angeles, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|eHARS|The selection of LA City residents is based on address at the time of diagnosis or at last report by each select year.||||1425.0|1458.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|1461.9|Boston, MA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS Surveillance Program, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|1476.0|Seattle, WA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|National HIV Surveillance system, including WA State DOH designation for prevalent cases||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|1918.8|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|2011.8|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2015|Male|All|2037.1|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|314.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|414.9|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|428.8|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||414.5|443.3
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|479.7|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||470.0|489.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|731.9|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|845.1|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||816.4|873.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|1072.6|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|1134.0|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|1252.5|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|All|2154.5|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|114.8|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|133.1|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|138.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|164.4|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||135.6|197.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|197.9|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|205.5|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||184.4|226.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|215.7|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||180.7|250.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Asian/PI|298.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|606.0|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||540.6|677.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1036.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1145.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1361.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||1309.4|1413.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1429.8|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1564.2|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||1489.7|1638.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1571.6|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1741.8|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|1894.8|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Black|3212.1|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|276.7|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|401.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||384.7|417.6
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|427.6|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|452.5|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|468.0|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||424.6|514.5
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|545.1|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||499.2|590.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|668.2|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|1542.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Hispanic|1614.6|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Multiracial|1453.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|233.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|276.1|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||238.3|313.9
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|364.5|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|520.5|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||372.1|708.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|551.4|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||444.0|677.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|583.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|679.2|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|Other|916.1|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|Includes Mixed Race, Asian/Pacific Islander, and American Indian/Alaska Native Populations||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|283.3|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|412.2|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||399.2|425.2
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|421.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|485.7|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||468.0|504.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|599.0|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|632.9|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|846.9|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||790.5|903.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|991.1|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|1081.0|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Both|White|1266.7|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|70.2|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||62.2|78.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|83.7|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|88.1|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|151.7|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||143.9|159.4
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|174.6|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|263.7|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|274.0|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|283.8|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||260.5|307.0
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|667.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Female|All|1069.3|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|544.9|Phoenix, AZ|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Arizona Department of Health Services (ADHS) annual reports||Maricopa County level data||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|742.2|San Diego County, CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Source: County of San Diego, Health & Human Services Agency, HIV/AIDS Epidemiology Unit, HIV/AIDS Reporting System, Received 1/2018; SANDAG, Current Population Estimates, Received 05/2017. Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics, 1//2018.|All HIV/AIDS data was pulled from the HIV/AIDS Reporting System. Crude rates per 100,000 population use SANDAG current population estimates for the specific year.|1. Living through year-end. 2. 2010-2015 include only cases that have had either an HIV diagnosis or an AIDS diagnosis as a resident of San Diego County.  2016 also includes those who have been diagnosed elsewhere but were living in San Diego in 2016. 3. Beginning in 2015, additional data sources allowed for more updated address information for People Living with HIV/AIDS, resulting in more accurate numbers currently living in San Diego County.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|793.7|Portland (Multnomah County), OR|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|ORPHEUS Data, w/ OPHAT Tot Pop query|||||766.3|821.8
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|803.4|Las Vegas (Clark County), NV|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.||||||785.7|821.1
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|1200.1|Dallas, TX|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|1441.0|Oakland (Alameda County), CA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|Alameda County eHARS, Q2 2017|||||1387.4|1494.7
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|1908.1|Philadelphia, PA|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 pop using 2010 US Census figures.|AACO team||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|1970.4|Denver, CO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|2056.6|Kansas City, MO|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|||||||
HIV/AIDS|Persons Living with HIV/AIDS Rate (Per 100,000 people)|2016|Male|All|3290.4|Washington, DC|Rate of persons living with HIV/AIDS at the end of a given year; crude rate per 100,000 population using 2010 US Census figures.|Enhanced HIV/AIDS Reporting System (eHARS)|Numerator estimate based on the number of individuals with a documented lab report (i.e., viral load and/or CD4) in the last five years with a resdiential address in the District at last lab.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|51.9|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|54.6|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|59.9|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2010 NC BRFSS (Mecklenburg Sample)|||||51.3|68.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|62.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|62.8|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2010-2012.||Adults aged 65+ who have ever had a pneumonia vaccination for the San Diego-Carlsbad-San Marcos, CA Metropolitan Statistical Area||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|73.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||65.0|80.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|All|76.3|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Behavior Risk Factors: Selected Metropolitan Area Risk Trends (SMART), Centers for Disease Control and Prevention||Tarrant County (not just Fort Worth)||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Asian/PI|43.5|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Asian/PI|72.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||PI not included|||37.0|91.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Black|41.5|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Black|47.6|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Black|54.2|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2010 NC BRFSS (Mecklenburg Sample)|||||34.5|74.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Black|55.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||26.0|81.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Hispanic|39.1|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Hispanic|62.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Other|54.6|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Other|65.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|Other||Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|White|61.1|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|White|62.6|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|White|65.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|White|66.2|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2010 NC BRFSS (Mecklenburg Sample)|||||57.4|74.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Both|White|77.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||69.0|84.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Female|All|51.5|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Female|All|54.0|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Female|All|60.6|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Female|All|70.1|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2010 NC BRFSS (Mecklenburg Sample)|||||60.9|79.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Female|All|79.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||69.0|87.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Male|All|41.7|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2010 NC BRFSS (Mecklenburg Sample)|||||26.1|57.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Male|All|48.5|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2010. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Male|All|64.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||52.0|74.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2010|Male|All|65.2|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|44.9|Long Beach, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source: 2011 Los Angeles County Health Survey.  Note: Estimates are based on self-reported data by a random sample of 8,036 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|47.5|Chicago, Il|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Behavioral Risk Factor Surveillance System 2011, Illinois Department of Public Health||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|60.6|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||50.6|69.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|62.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|63.3|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|63.3|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|63.9|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||58.5|68.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|65.1|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2011 NC BRFSS (Mecklenburg Sample)|||||56.6|73.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|66.4|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|67.7|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||54.0|81.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|68.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||61.8|75.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|70.1|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||62.7|76.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|71.2|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2010-2012.||Adults aged 65+ who have ever had a pneumonia vaccination for the San Diego-Carlsbad-San Marcos, CA Metropolitan Statistical Area||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|71.8|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|74.9|Minneapolis, MN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS SMART County Prevalence Data|Respondents aged 65 or older who reported having a pneumonia shot. Variable: _PNEUMO2|County data was used as a proxy (Hennepin County)||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|76.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|77.2|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||70.3|84.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|All|80.4|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Asian/PI|47.0|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Does not include Pacific Islander||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Asian/PI|59.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Asian/PI|83.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||65.1|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|45.5|Chicago, Il|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Behavioral Risk Factor Surveillance System 2011, Illinois Department of Public Health||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|47.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|52.4|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||35.9|68.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|55.2|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||45.2|64.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|56.8|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2011 NC BRFSS (Mecklenburg Sample)|||||33.1|78.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|57.6|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|67.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|67.6|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|74.5|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|74.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||57.6|92.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black|92.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic|43.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic|43.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||19.4|67.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic|48.6|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic|50.0|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic|52.0|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||35.3|68.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic|55.7|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||42.2|68.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Other|69.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Other|80.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||57.1|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|62.7|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|62.8|Chicago, Il|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Behavioral Risk Factor Surveillance System 2011, Illinois Department of Public Health||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|66.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|68.3|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2011 NC BRFSS (Mecklenburg Sample)|||||59.4|77.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|69.3|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||63.8|74.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|70.4|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|71.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||64.8|78.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|72.6|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|75.5|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|76.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|78.2|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||65.0|91.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|80.4|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Both|White|81.2|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||74.7|86.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|0.0|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|50.1|Chicago, Il|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Behavioral Risk Factor Surveillance System 2011, Illinois Department of Public Health||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|62.6|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||50.4|73.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|64.1|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|64.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|65.0|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|66.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||57.0|76.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|66.6|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||60.1|72.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|66.7|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|72.3|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2011 NC BRFSS (Mecklenburg Sample)|||||62.4|82.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|72.3|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||63.2|79.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|76.5|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|78.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Female|All|81.8|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|0.0|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|42.6|Chicago, Il|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Behavioral Risk Factor Surveillance System 2011, Illinois Department of Public Health||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|57.5|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2011 NC BRFSS (Mecklenburg Sample)|||||43.9|71.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|57.5|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||40.9|72.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|58.7|Los Angeles, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|59.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|59.6|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||50.2|68.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|60.6|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|65.1|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||51.9|76.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|66.0|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|69.3|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|70.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||61.7|79.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|74.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2011|Male|All|77.8|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|0.2|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|50.2|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|50.9|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||41.7|60.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|59.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|60.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||54.4|66.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|60.7|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||52.2|69.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|64.0|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|65.2|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2012 NC BRFSS (Mecklenburg Sample)|||||56.2|74.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|69.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|70.7|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||63.0|78.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|71.5|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||63.6|78.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|71.9|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||61.2|80.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|73.4|Minneapolis, MN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS SMART County Prevalence Data|Respondents aged 65 or older who reported having a pneumonia shot. Variable: _PNEUMO2|County data was used as a proxy (Hennepin County)||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|76.0|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|77.9|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||64.9|90.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|All|83.7|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Asian/PI|7.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||0.0|19.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Asian/PI|39.3|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|46.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|46.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||25.7|68.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|47.2|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||37.4|57.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|51.1|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|54.3|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2012 NC BRFSS (Mecklenburg Sample)|||||32.5|76.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|57.9|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|60.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black|80.1|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic|32.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||15.3|50.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic|41.4|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic|43.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic|47.6|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Hispanic||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Other|56.8|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Other|66.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||40.5|93.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Other||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|55.1|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|64.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|66.5|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||59.4|73.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|67.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|69.0|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2012 NC BRFSS (Mecklenburg Sample)|||||59.0|78.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|70.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||64.8|75.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|70.5|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|72.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|74.5|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|74.8|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||66.5|81.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|77.4|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||64.0|90.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|78.6|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||65.0|87.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|82.6|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||72.8|89.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Both|White|87.3|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|50.2|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|51.1|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||43.3|58.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|61.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||53.9|68.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|61.5|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2012 NC BRFSS (Mecklenburg Sample)|||||49.6|73.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|63.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|65.7|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|70.7|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||60.3|79.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|70.8|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|71.3|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|71.5|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2012.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||59.2|83.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|71.6|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||57.1|82.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|74.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|78.8|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||62.1|95.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Female|All|86.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|0.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|47.2|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||35.6|58.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|50.3|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|NYC DOHMH Community Health Survey (CHS) 2012. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|50.4|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||29.0|71.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|55.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|59.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||49.9|68.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|61.3|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|63.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|69.7|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2012 NC BRFSS (Mecklenburg Sample)|||||56.3|83.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|72.2|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||55.7|84.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|72.3|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||59.9|82.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|80.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All|83.4|Baltimore, MD|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC BRFSS|The three most recent years of available data are 2010-2012. Data from Baltimore City County.|Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2012|Male|All||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|0.2|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|41.1|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||29.7|53.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|46.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|53.8|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||48.6|58.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|57.4|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||47.1|67.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|59.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|60.8|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2013 NC BRFSS (Mecklenburg Sample)|||||49.4|72.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|60.8|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||50.6|70.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|62.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||55.8|68.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|64.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||58.1|70.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|64.4|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|68.1|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||55.3|80.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|71.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|73.0|San Jose, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|74.5|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source 2013 BRFSS , Bexar County||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|75.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|All|75.1|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||68.8|81.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Asian/PI|66.0|San Jose, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Asian/PI|74.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||39.8|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black|38.7|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2013 NC BRFSS (Mecklenburg Sample)|||||14.1|63.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black|38.9|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||26.3|53.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black|48.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black|56.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||33.2|80.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black|61.9|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black|76.9|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic|39.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic|40.1|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic|58.4|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source 2013 BRFSS , Bexar County||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic|61.4|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic|67.0|San Jose, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic|79.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||57.8|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Hispanic||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Other|36.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||0.0|72.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Other||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|59.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|60.2|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||54.6|65.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|62.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||55.2|69.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|63.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|63.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||57.1|70.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|63.8|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||52.6|73.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|64.1|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||54.6|73.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|64.2|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||48.7|79.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|69.3|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|70.8|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2013 NC BRFSS (Mecklenburg Sample)|||||58.9|82.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|73.3|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|75.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|79.0|San Jose, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|80.7|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Both|White|89.3|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source 2013 BRFSS , Bexar County||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|0.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|45.1|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||31.3|59.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|47.8|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|49.9|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||37.0|62.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|57.6|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||51.4|63.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|61.8|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2013 NC BRFSS (Mecklenburg Sample)|||||45.7|77.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|61.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|62.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||54.5|69.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|66.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||58.4|73.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|66.3|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|67.9|Houston, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Texas Behavioral Risk Factor Surveillance Systems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult age 65+ with pneumonia shot  from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, n), All reported rates were weighted for Texas demographics and the probability of selection. Calculated variable: pneumonia shot adults age 65+. Note: N=sample size less than 50, estimate not displayed.|Includes the Houston-Baytown-Sugarland MSA, not just Houston|||55.6|78.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|69.3|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||56.1|82.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|69.8|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source 2013 BRFSS , Bexar County||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|75.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|80.0|San Jose, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Female|All|80.7|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|0.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|36.5|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||19.9|57.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|44.5|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Florida Behavioral Risk Factor Surveillance System county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|47.9|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||39.2|56.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|57.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 75, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989_2013, http://www.cdc.gov/nchs/data/hus/hus14.pdf#075||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|59.7|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2013 NC BRFSS (Mecklenburg Sample)|||||45.7|77.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|61.4|Washington, DC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|DC BRFSS||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|62.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||52.1|72.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|65.0|San Jose, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|66.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|age 65+ ever had pneumonia vaccination|||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|67.7|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS||Due to changes in BRFSS sampling methodology, data from 2010 and before are not directly comparable to data from 2011 and beyond.  Male 2010 estimate suppressed because n<50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|67.8|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||51.0|84.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All|82.4|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source 2013 BRFSS , Bexar County||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2013|Male|All||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|0.2|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|6.9|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.1|9.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|54.9|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||43.8|65.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|59.0|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||53.6|64.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|59.0|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||50.4|67.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|61.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 69, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989-2014||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|64.7|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2014 NC BRFSS (Mecklenburg Sample)|||||55.9|73.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|64.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||59.4|70.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|67.5|Kansas City, MO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?||||||55.5|79.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|69.6|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||61.3|78.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|71.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||65.8|76.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|73.1|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||67.5|78.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|76.3|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||69.9|82.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|77.2|Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|used crude rates|2014 BRFSS|||70.8|83.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|All|81.7|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||70.1|93.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Asian/PI|6.0|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.2|14.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Asian/PI|98.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||95.0|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black|42.5|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2014 NC BRFSS (Mecklenburg Sample)|||||20.8|64.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black|49.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 69, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989-2015||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black|56.0|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||43.8|67.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black|56.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||38.0|75.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic|45.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 69, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989-2016||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic|62.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||38.8|86.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic|67.6|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||56.6|77.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Hispanic||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Other|67.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||20.3|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Other||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Other||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|7.1|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.0|10.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|57.7|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||47.7|67.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|64.0|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||58.0|70.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|64.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 69, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989-2017||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|66.0|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||59.8|71.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|70.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||64.6|76.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|75.3|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2014 NC BRFSS (Mecklenburg Sample)|||||66.2|84.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|76.2|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||69.8|81.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|78.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||71.2|84.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Both|White|82.8|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||69.6|96.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|0.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|6.3|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.1|9.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|53.2|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||41.6|64.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|57.0|Detroit, MI|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|Among adults aged 65 years and older, the proportion who reported that they ever had a pneumococcal vaccine.||||43.1|70.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|57.8|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||51.1|64.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|63.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 69, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989-2018||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|65.0|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||58.3|71.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|70.1|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2014 NC BRFSS (Mecklenburg Sample)|||||59.7|80.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|74.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||68.1|80.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|79.9|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||73.3|85.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|82.4|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||76.1|88.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|82.6|Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|used crude rates|2014 BRFSS|||75.6|89.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Female|All|86.4|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||76.9|95.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|0.1|Phoenix, AZ|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|7.9|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.8|12.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|55.8|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2014 NC BRFSS (Mecklenburg Sample)|||||40.6|71.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|58.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Table 69, Pneumococcal vaccination among adults aged 18 and over, by selected characteristics: United States, selected years 1989-2019||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|60.7|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||51.9|68.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|64.2|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||54.6|72.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|64.4|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||55.4|73.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|65.0|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2014.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||52.1|78.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|67.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||58.4|76.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|68.9|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||57.6|80.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All|69.7|Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|used crude rates|2014 BRFSS|||57.9|81.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2014|Male|All||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|8.2|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||6.2|10.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|55.4|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2015 NC BRFSS (Mecklenburg Sample)|||||43.2|67.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|59.0|Long Beach, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Source: 2015 Los Angeles County Health Survey.  Note: Estimates are based on self-reported data by a random sample of 8,008 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|60.1|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||53.2|66.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|64.2|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||59.9|68.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|64.7|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||50.9|78.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|68.4|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||60.8|75.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|69.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||62.9|76.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|72.4|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health||FW-Arlington Metropolitan Division includes residents from Hood, Johnson, Parker, Somervell, Tarrant, and Wise counties (Not just Tarrant County, TX)|||62.6|82.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|74.9|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||65.2|82.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|78.0|Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?||||||72.0|84.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|81.8|Kansas City, MO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|All|87.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||81.4|92.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Asian/PI|10.4|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||5.3|19.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Asian/PI|64.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||32.8|95.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Asian/PI||Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black|10.6|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.3|26.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black|36.4|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2015 NC BRFSS (Mecklenburg Sample)|||||13.0|57.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black|62.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||41.1|84.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black|65.3|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||56.4|73.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black||Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic|63.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||34.0|93.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic||Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Hispanic||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other|69.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||37.7|100.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other||Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|7.1|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.1|10.3
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|62.3|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||46.0|78.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|64.3|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||56.6|72.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|65.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||57.6|73.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|69.2|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||64.0|74.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|69.8|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2015 NC BRFSS (Mecklenburg Sample)|||||57.0|82.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|71.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||64.8|78.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|78.7|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||65.7|87.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White|86.3|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||78.9|93.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Both|White||Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|8.7|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||6.1|12.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|54.0|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2015 NC BRFSS (Mecklenburg Sample)|||||40.9|67.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|60.1|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||50.2|70.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|65.5|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||60.0|70.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|66.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||57.9|74.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|75.8|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||64.7|86.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|80.1|Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?||||||72.6|87.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|80.5|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||67.7|89.1
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All|87.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||80.0|93.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Female|All||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|7.3|Seattle, WA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.4|11.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|57.9|Charlotte, NC|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|2015 NC BRFSS (Mecklenburg Sample)|||||33.0|82.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|60.0|San Diego County, CA|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2015.|Adults over age 65 who responded yes to having had their pneumonia vaccination||||50.5|69.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|62.4|New York City, NY|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|New York State Behavioral Risk Factor Surveillance System (BRFSS), limited to New York City (region=2), 2013, 2014, 2015. The New York State BRFSS is an annual statewide telephone survey; the sample is designed to be representative of the non-institutionalized adult household population, aged 18 years and older, who have either a landline or cellular telephone. Analysis by NYC DOHMH Bureau of Epidemiology Services.||Data are not age adjusted.|||55.4|68.8
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|67.5|San Antonio, TX|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS survey data||Bexar County level data|||51.7|80.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|73.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Nevada BRFSS - Clark County|||||63.1|83.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|75.4|Portland (Multnomah County), OR|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?||||||65.3|85.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All|87.1|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||77.4|96.7
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2015|Male|All||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|All|75.7|Kansas City, MO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|All|76.3|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||67.0|85.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|All|78.9|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||73.6|84.2
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Black||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Black||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Hispanic||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Other||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|Other||Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|White|74.8|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||63.1|86.5
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Both|White|83.9|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||78.8|89.0
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Female|All|72.8|Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|BRFSS|||||60.0|85.6
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Female|All|81.0|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||75.0|86.9
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Male|All|76.3|Denver, CO|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|Colorado BRFSS|||||67.3|85.4
Infectious Disease|Percent of Adults 65 and Over Who Received Pneumonia Vaccine|2016|Male|All||Columbus, OH|BRFSS (or similar survey). Percentage of adults over age 65 responding yes to have you ever had a pneumonia vaccination?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|All|40.2|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2010 NC BRFSS (Mecklenburg Sample)|||||34.6|45.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|All |49.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||44.0|54.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|Asian/PI|55.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS||Does not include PI|||38.0|71.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|Black|29.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2010 NC BRFSS (Mecklenburg Sample)|||||19.4|39.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|Black|47.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||29.0|65.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|Hispanic|33.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||18.0|53.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|Other|52.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS||Multiple Race|||28.0|75.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|White|48.0|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2010 NC BRFSS (Mecklenburg Sample)|||||41.2|54.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Both|White|49.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||43.0|56.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Female|All|43.0|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2010 NC BRFSS (Mecklenburg Sample)|||||36.1|49.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Female|All|53.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||46.0|60.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Male|All|36.1|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2010 NC BRFSS (Mecklenburg Sample)|||||27.4|44.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2010|Male|All |45.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||38.0|53.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|26.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||23.5|28.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|26.4|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||21.9|31.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|32.4|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|32.8|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|33.1|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||30.5|35.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|34.6|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2011 NC BRFSS (Mecklenburg Sample)|||||29.9|39.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|36.0|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||32.2|39.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|36.0|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||30.3|41.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|36.1|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||28.9|43.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|37.6|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||33.3|42.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|37.7|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|38.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|38.7|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|38.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||38.4|39.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|39.9|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|All|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|American Indian/Alaska Native|42.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||37.7|47.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|American Indian/Alaska Native|52.0|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|American Indian alone||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|American Indian/Alaska Native|63.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|American Indian alone||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|29.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||17.8|40.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|31.2|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||18.9|43.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|34.9|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||17.7|52.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|36.3|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|Does not include Pacific Islander||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|37.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Asian alone; Value reported is for the 2011-2012 flu season|||33.8|40.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|41.9|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|47.8|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI|58.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|23.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||14.8|31.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|24.6|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||20.0|30.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|25.8|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2011 NC BRFSS (Mecklenburg Sample)|||||17.4|34.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|27.7|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||13.0|42.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|29.0|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||22.5|36.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|30.3|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|32.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||31.1|34.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|35.3|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||21.0|52.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|36.2|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|36.5|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|38.9|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black|60.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|18.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||13.1|24.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|24.7|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|24.9|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|26.4|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||21.4|32.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|29.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||27.8|31.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|29.5|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||22.1|37.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|31.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|31.6|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|32.8|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||26.6|39.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|35.6|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|40.4|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|52.3|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||36.9|67.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Multiracial|37.0|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||28.1|46.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Multiracial|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other|24.2|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other|26.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||11.6|41.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other|35.8|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other|60.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|30.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||26.8|33.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|31.9|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||18.7|49.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|34.3|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|36.3|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||27.9|44.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|39.0|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||35.5|42.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|39.2|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|39.6|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||35.5|43.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|40.7|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||32.0|49.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|41.1|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|41.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||41.5|42.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|42.7|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|43.8|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|44.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2011 NC BRFSS (Mecklenburg Sample)|||||38.0|50.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|44.8|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||38.7|51.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|48.3|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Both|White|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|29.4|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||23.1|36.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|29.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||25.6|33.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|32.5|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|34.0|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2011 NC BRFSS (Mecklenburg Sample)|||||26.8|41.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|35.0|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||27.3|42.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|36.0|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|37.5|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||32.3|42.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|37.5|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|38.0|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||28.7|47.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|38.3|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||34.8|42.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|38.6|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||33.3|44.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|40.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|41.4|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|42.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||41.4|42.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|42.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Female|All|44.4|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|22.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||19.3|26.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|23.2|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||17.1|30.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|27.8|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||24.1|31.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|29.5|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|32.4|Los Angeles, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Los Angeles County Health Survey, 2011|Last analyzable database from 2011 interview cycle. Los Angeles City defined first by 999 census tracts and then, for those with missing census tracts, by 182 zip codes.|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|32.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||27.9|37.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|33.6|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|34.0|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||23.0|44.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|34.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS, 2011-2012|Respondents were asked: During the past 12 months| have you (child) had a flu shot? ANSWERED- Has had flu vaccine in past 12 months & ADULT 18+|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|34.9|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2011 NC BRFSS (Mecklenburg Sample)|||||29.0|40.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|35.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage_1112estimates.htm|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2011-2012 flu season|||34.6|36.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|36.9|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||27.6|46.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|37.8|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||30.9|45.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|37.9|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|38.3|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2011|Male|All|39.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|5.6|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|23.1|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||19.0|27.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|27.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||25.2|30.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|32.8|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||29.4|36.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|36.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||34.2|39.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|36.9|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|37.3|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||30.2|44.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|37.4|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2012 NC BRFSS (Mecklenburg Sample)|||||33.1|41.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|37.5|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||32.0|43.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|39.3|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|40.2|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||33.6|46.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|41.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|42.1|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|All|62.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|American Indian/Alaska Native|43.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||American Indian alone||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI|32.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||20.8|44.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI|34.2|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||23.0|45.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI|43.4|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI|43.6|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||31.8|55.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI|49.9|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI|57.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|21.6|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||17.3|26.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|22.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||14.9|29.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|27.4|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||19.6|36.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|28.6|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||15.1|42.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|31.6|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2012 NC BRFSS (Mecklenburg Sample)|||||22.9|40.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|31.8|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|34.4|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||19.2|49.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|41.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|52.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black|57.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|21.4|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||16.0|28.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|21.7|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|22.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||18.1|27.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|29.8|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|30.7|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||26.0|35.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|33.3|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|33.8|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||25.8|42.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|40.8|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|42.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic|75.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Multiracial|30.8|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||18.6|46.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other|18.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||8.4|27.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other|24.3|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other|34.0|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other|35.3|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 1/2018)|Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable|||17.5|53.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other|60.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|31.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||28.1|34.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|33.7|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||21.2|49.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|34.4|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|37.3|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||30.0|45.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|37.7|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|39.5|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||27.0|52.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|41.6|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||37.9|45.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|41.8|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2012 NC BRFSS (Mecklenburg Sample)|||||35.7|46.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|43.8|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||36.1|51.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|44.8|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||39.7|50.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|45.1|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|45.2|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|46.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Both|White|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|3.3|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|23.8|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||19.0|29.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|28.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||25.6|32.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|34.2|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||29.5|39.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|38.9|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|39.6|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||31.8|47.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|40.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|40.6|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|41.4|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||38.1|44.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|41.9|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2012 NC BRFSS (Mecklenburg Sample)|||||35.9|47.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|44.6|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||35.5|53.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|47.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|47.7|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Female|All|47.9|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||38.2|57.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|22.2|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||15.7|30.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|26.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||22.8|29.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|29.3|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|31.3|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||26.4|36.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|31.8|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||21.8|41.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|32.0|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2012 NC BRFSS (Mecklenburg Sample)|||||25.9|38.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|34.7|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|35.4|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||27.9|43.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|35.7|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||26.3|45.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|36.3|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|37.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|37.9|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2012|Male|All|42.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|8.4|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|25.4|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Florida Behavioral Risk Factor Surveillance System (BRFSS) county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|28.5|Chicago, Il|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|28.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||25.9|31.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|29.8|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||24.6|35.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|32.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||29.4|35.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|33.9|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||29.9|38.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|35.3|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|||||29.8|40.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|36.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2013 NC BRFSS (Mecklenburg Sample)|||||31.3|41.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|38.5|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||33.8|43.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|38.5|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|41.0|San Jose, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|41.2|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|42.2|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|44.4|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|45.6|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||39.6|51.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|49.4|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||47.3|51.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|All|59.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|American Indian/Alaska Native|48.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||American Indian alone||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI|24.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||14.5|35.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI|43.8|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI|46.4|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||38.4|54.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI|51.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Asian/PI||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|15.5|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Florida Behavioral Risk Factor Surveillance System (BRFSS) county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|20.3|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|23.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||14.7|32.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|23.7|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||17.4|30.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|28.5|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||22.8|35.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|31.4|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||21.1|43.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|31.6|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|32.0|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2013 NC BRFSS (Mecklenburg Sample)|||||22.3|41.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|32.0|San Jose, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|35.4|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||24.7|46.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|39.2|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|40.8|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|41.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|43.3|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||39.4|47.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black|61.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Black||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who responded yes to having had the seasonal flue shot or spray in past 12 months|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|23.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||16.6|30.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|26.9|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Florida Behavioral Risk Factor Surveillance System (BRFSS) county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|27.7|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||20.7|36.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|28.4|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|33.6|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|34.2|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|||||22.4|45.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|39.3|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|40.9|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|44.3|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|51.5|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||46.6|56.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic|53.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Multiracial|40.9|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||25.9|57.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other|12.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||3.6|22.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other|32.6|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other|34.1|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other|59.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|Adults who responded yes to having had the seasonal flue shot or spray in past 12 months|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. San Diego County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|34.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||30.7|38.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|36.4|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|36.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||32.6|40.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|37.5|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Florida Behavioral Risk Factor Surveillance System (BRFSS) county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|38.2|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|||||31.9|44.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|38.6|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||33.1|44.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|39.2|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||25.6|54.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|41.7|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|43.0|San Jose, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|46.1|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2013 NC BRFSS (Mecklenburg Sample)|||||38.9|53.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|47.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|47.9|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|49.4|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|50.5|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||42.8|58.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|52.0|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||48.7|55.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|52.5|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||45.0|59.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Both|White|59.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|4.9|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|24.6|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Florida Behavioral Risk Factor Surveillance System (BRFSS) county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|30.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||26.8|34.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|32.6|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|||||25.6|39.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|35.6|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||28.4|43.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|37.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||32.7|41.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|37.7|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||31.9|43.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|38.7|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||32.8|45.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|40.4|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|40.5|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2013 NC BRFSS (Mecklenburg Sample)|||||33.3|47.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|43.2|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|45.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|46.0|San Jose, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|47.4|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|48.8|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|49.3|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||41.0|57.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Female|All|53.2|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||50.3|56.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|3.5|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|22.9|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||16.3|31.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|26.3|Miami (Miami-Dade County), FL|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Florida Behavioral Risk Factor Surveillance System (BRFSS) county-level telephone survey conducted by the CDC and Florida Department of Health Bureau of Epidemiology||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|27.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||22.7|31.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|27.5|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||22.9|32.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|29.7|Houston, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Texas Behavioral Risk Factor Surveillance SYstems, Statewide BRFSS survey, 2013. Prepared by: Texas BRFSS, Center for Health Statisitics, Texas Department of State Health Services at May 12, 2015.|Adult Immunization in the past 12 months from Houston -Baytown-Sugarland MSA. Data were displayed with Percent (CI 95%, sample size n), All reported rates were weighted for Texas demographics and the probability of selection. Question: During the past 12 months, have you had either a seasonal flue shot or a seasonal flu vaccine that was sprayed in your nose?|Includes all of Houston-Baytown-Sugarland MSA, not just Houston|||24.2|35.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|31.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2013 NC BRFSS (Mecklenburg Sample)|||||24.6|38.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|36.2|Washington, DC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|DC BRFSS||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|37.0|San Jose, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|38.0|Seattle, WA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flushot in the past 12 months, age-adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|38.2|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||30.9|46.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|38.5|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|California Behavioral Risk Factor Survey Workgroup, Public Health Survey Research Program, California State University, Sacramento, CA Dept. of Public Health. BRFSS Data. 2013.|||||30.0|47.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|39.3|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS) 2011. The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|Self-reported vaccination; percent is age adjusted|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|41.3|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||32.3|50.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|41.6|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Weighted sample of adults 18+years of age who replied yes to the question During the past 12 months have you had either a seasonal flu shot or a season flu vaccine that was sprayed in your nose?|||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|43.5|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2013|Male|All|45.2|Boston, MA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Boston Behavioral Risk Factor Survey, Boston Public Health Commission|Boston Behavioral Risk Factor Survey used. The percent estimate is in response to During the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||42.0|48.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|0.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||46.5|63.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|5.3|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|23.6|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||19.1|28.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|32.2|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||29.1|35.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|35.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||32.0|38.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|36.7|Kansas City, MO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||||30.5|42.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|39.7|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||36.4|43.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|40.0|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|used crude rates|2014 BRFSS|||35.7|44.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|40.1|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||33.2|46.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|43.5|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||42.1|45.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|43.6|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||40.1|47.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|43.6|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||39.8|47.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|43.6|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Flu vaccination coverage for adults 18 years and older|Value reported is for the 2014-2015 flu season|||43.2|44.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All|54.7|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||45.3|64.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|All |44.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||39.0|48.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|American Indian/Alaska Native|40.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Value reported is for the 2014-2015 flu season|||36.6|44.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|0.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||43.6|75.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|37.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||23.4|51.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|39.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS||Does not include PI|||25.0|54.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|42.6|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||38.5|46.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|43.5|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||29.6|57.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|44.4|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Asian alone; Value reported is for the 2014-2015 flu season|||40.7|48.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI|76.1|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||62.5|89.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|0.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||39.8|78.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|22.6|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||17.8|28.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|25.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||11.0|47.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|25.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||16.6|35.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|27.5|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||15.8|39.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|28.9|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2014 NC BRFSS (Mecklenburg Sample)|||||20.0|37.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|29.7|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||23.3|36.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|34.0|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||23.7|46.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|38.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Value reported is for the 2014-2015 flu season|||37.1|40.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|40.6|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||37.5|43.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|46.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||22.5|71.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black|47.3|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||35.8|58.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|29.7|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||22.9|36.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|33.1|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||25.7|40.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|34.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||17.0|57.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|35.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Value reported is for the 2014-2015 flu season|||33.2|36.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|38.2|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||33.4|43.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|42.2|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||35.5|48.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|44.7|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||42.0|47.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic|47.0|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||26.8|67.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other|23.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||10.0|37.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other|29.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS||Multiple Race|||10.0|60.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other|33.0|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||24.1|43.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other|47.7|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County. CHIS notes this rate to be unstable|||13.9|81.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other||Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|0.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||45.0|69.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|33.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||30.0|37.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|38.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||34.4|42.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|45.0|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||40.1|50.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|45.1|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||42.3|47.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|45.6|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||37.0|54.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|45.8|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2014 NC BRFSS (Mecklenburg Sample)|||||39.0|52.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|46.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||42.0|50.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|46.7|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Value reported is for the 2014-2015 flu season|||47.1|47.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|48.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||43.0|53.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|49.2|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||36.3|62.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Both|White|49.9|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||44.4|55.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||35.2|58.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|3.1|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|27.3|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||20.8|34.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|33.9|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||29.5|38.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|36.6|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||32.6|40.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|41.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2014 NC BRFSS (Mecklenburg Sample)|||||34.6|48.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|43.0|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|used crude rates|2014 BRFSS|||36.8|49.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|44.2|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||39.6|48.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|45.6|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||36.5|54.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|47.0|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||44.9|49.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|47.0|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Value reported is for the 2014-2015 flu season|||46.4|47.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|48.3|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||42.0|54.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|49.2|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||44.3|54.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|52.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||46.0|59.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Female|All|57.4|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||45.1|69.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|0.6|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||51.9|73.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|2.2|Phoenix, AZ|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AZ BRFSS||All Maricopa County||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|19.3|Detroit, MI|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|Flu Shot in Past Year (18+ yrs)||||13.5|26.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|30.6|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||26.0|35.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|33.1|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2014 NC BRFSS (Mecklenburg Sample)|||||26.1|40.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|33.1|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||23.1|43.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|33.4|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||28.8|38.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|35.0|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||30.3|40.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|37.1|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|used crude rates|2014 BRFSS|||31.3|42.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|38.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||33.8|43.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|38.7|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||33.1|44.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|39.8|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||37.7|41.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|40.1|U.S. Total, U.S. Total|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|National Immunization Survey-Flu (NIS-Flu) and Behavioral Risk Factor Surveillance System (BRFSS), accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data||Value reported is for the 2014-2015 flu season|||39.3|40.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All|57.9|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||44.8|71.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2014|Male|All |36.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||30.0|42.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||42.0|61.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|29.4|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||25.9|33.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|32.9|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||26.4|39.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|37.7|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||33.5|41.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|41.3|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||38.8|43.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|42.1|Long Beach, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Source: 2015 Los Angeles County Health Survey.  Note: Estimates are based on self-reported data by a random sample of 8,008 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|43.3|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||||72.6|87.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|43.8|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||42.4|45.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|43.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||39.0|48.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|47.3|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||41.6|53.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|48.8|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||43.2|54.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|48.9|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2015 NC BRFSS (Mecklenburg Sample)|||||43.0|54.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|49.7|Kansas City, MO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All|49.7|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||34.6|64.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|All |41.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||38.0|45.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||27.0|72.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI|19.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||8.5|30.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI|34.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS||Does not include PI|||24.0|46.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI|44.9|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||25.4|64.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI|45.7|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||30.8|61.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI|50.6|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||47.1|54.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Asian/PI||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||28.5|62.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|22.8|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||13.6|32.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|25.6|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||14.1|37.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|28.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||16.0|46.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|34.6|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||28.6|41.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|35.8|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||26.2|45.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|39.2|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||36.5|41.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|41.0|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2015 NC BRFSS (Mecklenburg Sample)|||||30.8|51.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|41.0|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||21.6|60.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black|41.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||21.5|62.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Black||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|25.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||17.8|32.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|32.7|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||27.3|38.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|34.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||21.0|51.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|39.7|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||32.6|46.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|41.5|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||33.5|49.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|46.0|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||43.6|48.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|46.7|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||34.6|58.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic|64.4|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County. CHIS notes this rate to be unstable|||42.3|86.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Hispanic||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other|0.3|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County. CHIS notes this rate to be unstable|||0.9|51.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other|24.6|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||14.9|37.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other|35.1|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||14.4|55.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other|42.1|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||33.6|51.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other|46.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS||Multiple Race|||28.0|64.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other|55.4|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||31.8|78.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other||Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other||San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other||San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|Other||San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||35.7|62.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|35.0|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||30.0|39.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|35.8|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||27.5|44.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|40.2|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||35.3|45.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|42.3|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||39.8|44.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|45.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||41.0|49.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|46.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||41.0|52.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|47.8|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||44.7|50.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|51.2|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||44.0|58.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|57.8|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2015 NC BRFSS (Mecklenburg Sample)|||||49.0|66.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|57.8|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||40.2|75.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White|58.2|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||49.5|66.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Both|White||Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||43.3|63.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|28.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2015 NC BRFSS (Mecklenburg Sample)|||||21.5|35.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|29.5|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||24.9|34.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|40.9|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||32.0|49.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|43.2|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||39.9|46.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|44.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||39.0|49.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|45.3|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||39.3|51.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|45.9|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||38.5|53.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|47.3|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||||41.3|53.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|47.4|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||45.6|49.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|49.9|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||41.9|57.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|55.6|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||47.8|63.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Female|All|57.4|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||41.1|73.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|0.5|Oakland (Alameda County), CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|AskCHIS||Data is for Alameda County|||35.8|64.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|25.4|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||16.6|34.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|28.3|Charlotte, NC|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|2015 NC BRFSS (Mecklenburg Sample)|||||20.2|36.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|29.1|Indianapolis (Marion County), IN|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CDC Behavioral Risk Factor Surveillance System (BRFSS) data|||||24.0|34.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|29.3|Las Vegas (Clark County), NV|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Nevada BRFSS - Clark County|||||24.0|34.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|37.7|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months||||29.3|46.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|39.2|Fort Worth (Tarrant County), TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Tarrant County BRFSS, 2015, Tarrant County Public Health|||||35.6|42.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|39.2|Portland (Multnomah County), OR|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?||||||33.2|45.2
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|39.8|New York City, NY|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|NYC DOHMH Community Health Survey (CHS). The CHS is a cross-sectional telephone survey of adults aged 18 and older from all five boroughs of New York City. Data are collected from selected respondents with landline telephones and cell phones (since 2009). Interviews are conducted in English, Spanish, Russian, and Chinese (Mandarin and Cantonese). All data collected are self-reported. Data are weighted to the NYC adult population.|||||37.9|41.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|42.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||34.5|50.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|43.2|San Francisco, CA|BRFSS (or similar survey). Percent of adults 18 years and older responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|CHIS|||||18.8|67.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All|48.9|San Antonio, TX|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS survey data||Bexar County level data|||40.0|57.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2015|Male|All |39.0|Seattle, WA|Percent of adults vaccinated for annual flu|BRFSS|||||34.0|44.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|All|38.0|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||32.2|43.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|All|38.3|Kansas City, MO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|All|42.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||39.2|45.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|All|45.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||40.9|50.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Asian/PI|36.9|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||21.9|51.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Asian/PI||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Asian/PI||Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|Colorado BRFSS|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Black|32.4|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||21.9|42.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Black|38.4|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||25.7|51.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Black|55.0|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||37.3|72.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Hispanic|37.1|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||30.2|44.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Hispanic|38.7|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||32.0|45.5
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Hispanic||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <50.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Other|47.8|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable|||21.4|74.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Other|51.3|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||36.8|65.9
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|Other||Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|White|42.4|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||34.8|50.0
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|White|44.5|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||40.3|48.7
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Both|White|52.7|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||45.6|59.8
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Female|All|42.7|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||34.8|50.6
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Female|All|47.2|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||40.4|54.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Female|All|47.3|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||42.6|52.1
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Male|All|33.1|Columbus, OH|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|BRFSS|||||24.9|41.4
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Male|All|37.7|Denver, CO|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|Colorado BRFSS|||||33.0|42.3
Infectious Disease|Percent of Adults Who Received Seasonal Flu Shot|2016|Male|All|44.3|San Diego County, CA|BRFSS (or similar survey). Percent of adults over age 18 responding yes to during the past 12 months| have you had either a flu shot or a flu vaccine that was sprayed in your nose?|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|Vaccinated for flu in the past 12 months||||36.7|51.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|All|46.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||45.2|48.7
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|American Indian/Alaska Native|46.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||31.3|65.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|Asian/PI|56.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Asian alone; Value reported is for the 2010-2011 flu season|||50.3|62.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|Black|46.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||41.8|51.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|Hispanic|50.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||47.0|54.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|Multiracial|45.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||38.4|52.8
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Both|White|44.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||42.5|47.0
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Female|All|48.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||45.9|50.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2010|Male|All|45.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2010-2011 flu season|||43.2|48.3
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|All|13.6|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.||2011, 2012, 2013; percent of population under age 18, vaccinated for flu. This is the proportion of active patients registered in the WA State Immunization Information System registry (i.e Child Profile) that have received one valid flu dose at any time (used midpoint of year - june 30th)|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|All|47.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||45.3|48.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|All|48.1|Long Beach, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Source: 2011 Los Angeles County Health Survey.  Note: Estimates are based on self-reported data by a random sample of 8,036 Los Angeles County adults, representative of the adult population in Los Angeles County. The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided.||Originating dataset did not include gender and race estimates.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|All|49.7|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|California Health Interview Survey, 2011-12|Countywide data only.  The flu question was asked only of parents of children age 6 months through 11 years of age in 2011-2012.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|All|51.1|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||40.3|61.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|American Indian/Alaska Native|59.8|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||45.5|74.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Asian/PI|62.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Asian alone; Value reported is for the 2011-2012 flu season|||55.4|69.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Asian/PI|70.6|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|California Health Interview Survey, 2011-12|Countywide data only.  The flu question was asked only of parents of children age 6 months through 11 years of age in 2011-2012.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Asian/PI||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Black|45.3|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|California Health Interview Survey, 2011-12|Countywide data only.  The flu question was asked only of parents of children age 6 months through 11 years of age in 2011-2012.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Hispanic|48.4|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|California Health Interview Survey, 2011-12|Countywide data only.  The flu question was asked only of parents of children age 6 months through 11 years of age in 2011-2012.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Hispanic|50.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||46.9|53.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Hispanic|61.0|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||46.8|75.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Hispanic||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Multiracial|49.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||43.0|57.2
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Multiracial|57.9|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|California Health Interview Survey, 2011-12|Countywide data only.  The flu question was asked only of parents of children age 6 months through 11 years of age in 2011-2012.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|Other||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|White|41.4|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||25.8|57.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|White|44.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||41.5|46.7
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|White|46.8|Los Angeles, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|California Health Interview Survey, 2011-12|Countywide data only.  The flu question was asked only of parents of children age 6 months through 11 years of age in 2011-2012.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Both|White||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Female|All|47.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||44.6|49.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Female|All|50.2|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||31.9|68.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Female|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Male|All|47.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|NHIS (CDC/NCHS)|NHIS; Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2011-2012 flu season|||44.7|49.7
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Male|All|52.0|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||39.5|64.4
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2011|Male|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|6.5|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|17.3|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.||2011, 2012, 2013; percent of population under age 18, vaccinated for flu. This is the proportion of active patients registered in the WA State Immunization Information System registry (i.e Child Profile) that have received one valid flu dose at any time (used midpoint of year - june 30th)|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|35.1|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, computerized information system that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City. Fo rmore information, visit nyc.gov/health/cir.|Denominator is NYC DOHMH 2012 population estimates, updated 2014; the percent vaccinated for flu is calculated from the count of children less than 18 years old who received at least one dose of flu vaccine in New York City as of December 31st, 2012 (numerator), among all NYC resident children less than 18 years old (denominator)|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|35.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|42.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|54.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||47.6|62.0
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All|56.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm#data||Value reported is for the 2012-2013 flu season|||55.7|57.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|American Indian/Alaska Native|28.5|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|American Indian alone; Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|American Indian/Alaska Native|34.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health|Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.|American Indian alone||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Asian/PI|29.7|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Asian/PI|32.9|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Asian/PI||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Black|27.5|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Black|31.9|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Hispanic|28.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Hispanic|32.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Hispanic|51.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2013. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||41.0|62.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Hispanic||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Other|48.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|Other||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|White|31.1|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|White|33.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health|Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|White|61.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2013. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||49.1|74.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Both|White||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Female|All|3.2|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Female|All|35.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Female|All|41.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Female|All|51.5|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2013. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||39.0|64.0
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Female|All|56.7|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm#data||Value reported is for the 2012-2013 flu season|||55.5|56.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Female|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Male|All|3.3|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Male|All|35.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Male|All|41.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Male|All|56.4|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm#data||Value reported is for the 2012-2013 flu season|||55.2|57.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Male|All|57.6|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2013. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||47.9|67.4
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2012|Male|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|6.8|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|24.7|Seattle, WA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.||2011, 2012, 2013; percent of population under age 18, vaccinated for flu. This is the proportion of active patients registered in the WA State Immunization Information System registry (i.e Child Profile) that have received one valid flu dose at any time (used midpoint of year - june 30th)|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|37.7|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|40.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, computerized information system that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City. Fo rmore information, visit nyc.gov/health/cir.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|58.9|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||58.1|59.7
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|62.0|San Jose, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Santa Clara County level data|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|All|64.0|Chicago, Il|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|American Indian/Alaska Native|32.3|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|American Indian alone; Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|American Indian/Alaska Native|35.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health|Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.|American Indian alone||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|American Indian/Alaska Native|65.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||59.9|71.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Asian/PI|33.6|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Asian/PI|40.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Asian/PI|70.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Asian alone; Value reported is for the 2013-2014 flu season|||67.4|73.8
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Asian/PI|73.0|San Jose, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Santa Clara County level data|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Black|27.6|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Black|34.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Black|57.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||55.0|59.4
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Hispanic|32.0|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Hispanic|32.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health|Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Hispanic|60.0|San Jose, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14|Santa Clara County level data|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Hispanic|66.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||63.9|68.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|Other|51.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|White|35.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|White|35.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|White|53.0|San Jose, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||Santa Clara County level data||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Both|White|55.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||54.3|53.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Female|All|3.3|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Female|All|37.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Female|All|44.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Female|All|59.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||58.2|60.4
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Female|All|64.0|San Jose, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||Santa Clara County level data||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Male|All|37.6|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Division of Disease Control, Philadelphia Dept of Public Health||Numerator data obtained from KIDS Plus IIS, denominator data from 2010 census.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Male|All|44.0|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Male|All|58.6|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|http://www.cdc.gov/flu/fluvaxview/coverage-1314estimates.htm||Value reported is for the 2013-2014 flu season|||57.5|59.7
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2013|Male|All|60.0|San Jose, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Santa Clara County Public Health Department, Behavioral Risk Factor Surveillance Survey, 2013-14||Santa Clara County level data||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All|5.4|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All|36.2|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All|40.8|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, computerized information system that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City. Fo rmore information, visit nyc.gov/health/cir.|Denominator is NYC DOHMH 2012 population estimates, updated 2014; the percent vaccinated for flu is calculated from the count of children less than 18 years old who received at least one dose of flu vaccine in New York City as of December 31st, 2012 (numerator), among all NYC resident children less than 18 years old (denominator)|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All|49.9|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All|54.1|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||45.7|62.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All|59.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||58.5|60.1
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|American Indian/Alaska Native|24.1|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|American Indian alone; Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|American Indian/Alaska Native|67.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||61.1|72.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Asian/PI|28.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Asian/PI|55.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Asian/PI|72.1|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Asian alone; Value reported is for the 2014-2015 flu season|||68.6|75.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Asian/PI||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Black|23.1|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Black|49.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Black|58.3|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||55.8|60.8
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Hispanic|26.4|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|For inclusion in the numerator, the age (0-17) was calculated for each respective year and each individual who was 0-17 years of age and had at least 1 influenza vaccine in that year was counted. Patients with  contraindications for influenza vaccine were excluded. Population estimates calculated by the Denver Office of Local Affairs were used for the denominators.|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Hispanic|53.8|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Hispanic|62.2|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||51.1|73.2
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Hispanic|64.2|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||62.4|66.0
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Hispanic||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Other|42.7|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|Other||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|White|28.9|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|White|38.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||25.0|52.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|White|51.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|White|56.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||55.1|56.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Both|White||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Female|All|2.7|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Female|All|36.6|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Female|All|50.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Female|All|51.4|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||40.6|62.3
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Female|All|59.0|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||58.0|60.0
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Female|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Male|All|2.8|Phoenix, AZ|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Male|All|35.5|Denver, CO|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CIIS (Colorado Immunization Information System),  Population estimates (denominator) from DOLA (Denver Office of Local Affairs)|Limitations: DOLA population estimates (based on US 2010 Census data) and CIIS do not have the same race categories; high percent of missing and unknown race & gender data in CIIS|||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Male|All|49.9|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2014 would have started around August 2014 and run through March 2015. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Male|All|56.9|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||44.3|69.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Male|All|59.5|U.S. Total, U.S. Total|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|National Immunization Survey-Flu (NIS-Flu) and BRFSS; Accessed via http://www.cdc.gov/flu/fluvaxview/coverage-1415estimates.htm#data|Percent of children aged 6 months through 17 years who are vaccinated annually against seasonal influenza|Value reported is for the 2014-2015 flu season|||58.4|60.6
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2014|Male|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|All|41.1|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, immunization information system (IIS) that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|All|50.2|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||32.6|67.8
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|All|52.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Asian/PI|57.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Asian/PI||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Asian/PI||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Black|51.9|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|CHIS Statistically unstable.YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Black||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Hispanic|54.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Hispanic||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Hispanic||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Other|46.2|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|Other=American Indian/Alaskan Native and Two or more races (not hispanic) 2014 and 2015 statistically unstable; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|Other||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|White|46.5|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||20.7|72.3
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|White|54.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Both|White||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Female|All|41.1|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, immunization information system (IIS) that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Female|All|50.3|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||26.1|74.5
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Female|All|52.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Female|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Male|All|41.1|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, immunization information system (IIS) that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Male|All|50.0|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||31.1|68.9
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Male|All|52.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2015 would have started around August 2015 and run through March 2016. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2015|Male|All||San Francisco, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|CHIS||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|All|39.1|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, immunization information system (IIS) that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|All|49.3|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2010. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||38.2|60.4
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|All|51.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Asian/PI|56.4|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Black|50.0|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Black||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Hispanic|41.7|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2010. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||25.0|58.3
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Hispanic|53.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Other|49.1|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|Other||San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|YRBSS was not used since it is only representative of San Diego City (not San Diego County); Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|White|55.5|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Both|White|66.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2010. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||47.7|85.8
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Female|All|39.0|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, immunization information system (IIS) that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Female|All|41.1|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2010. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||24.4|57.8
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Female|All|51.3|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Male|All|39.1|New York City, NY|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|New York Citywide Immunization Registry (CIR), a confidential, population-based, immunization information system (IIS) that collects and consolidates immunization records of people vaccinated at healthcare facilities within New York City.||||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Male|All|51.2|Philadelphia, PA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|Philadelphia Immunization Program ||Flu season for 2016 would have started around August 2016 and run through March 2017. Flu season for 2016 would have started around August 2016 through March 2017. Those few months in 2017 would make a difference in the numbers.||||
Infectious Disease|Percent of Children Who Received Seasonal Flu Shot|2016|Male|All|57.8|San Diego County, CA|YRBS/YRBSS (or similar survey). Percent of population under age 18 vaccinated for seasonal flu.|UCLA Center for Health Policy Research, California Health Interview Survey, http://www.chis.ucla.edu/ (accessed 3/2017)|< 17 years of age Vaccinated for flu in the past 12 months|No CHIS data available for 2010. YRBSS was not used since it is only representative of San Diego City (not San Diego County)|||40.0|75.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.1|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.2|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||6.1|11.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.2|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.7|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.7|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||10.4|15.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|13.4|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|13.9|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.0|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.7|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2010-2012|||13.3|16.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.8|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|15.1|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|16.4|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates||||12.6|20.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|18.7|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|19.1|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.5|23.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|21.1|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||18.8|23.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|41.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||37.9|44.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|15.2|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|6.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|7.8|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||3.1|18.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|13.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|13.9|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||7.3|20.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|15.0|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|16.0|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|16.1|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2010-2012|||13.9|18.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|21.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates||||13.5|33.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|0.0|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.7|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|11.8|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|12.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|13.7|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|15.1|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|15.2|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|15.4|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2010-2012|||10.3|22.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|16.9|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates||||10.3|26.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|17.6|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|22.6|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|27.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||18.0|36.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|34.7|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|49.7|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||36.1|66.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.7|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.5|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|10.3|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|12.3|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|13.2|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|13.6|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|14.0|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||8.9|19.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|16.6|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|43.6|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||38.4|48.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|6.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||0.0|16.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|8.0|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||5.7|11.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|8.2|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.3|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.4|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.0|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.5|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|12.1|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.6|15.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.2|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.2|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.2|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.3|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates||||8.9|21.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.3|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2010-2012|||12.3|16.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|16.4|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|17.9|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|19.5|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|22.7|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||19.9|25.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|23.2|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.5|28.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|38.7|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||34.1|43.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.6|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|7.2|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.7|11.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|7.4|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|8.5|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|9.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|10.0|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|10.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|10.7|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||8.0|14.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|10.9|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2010-2012|||9.4|12.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|11.5|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates||||7.8|16.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|11.7|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|11.7|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|11.9|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|12.1|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|12.4|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|12.9|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|16.3|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.1|21.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|16.9|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||14.2|19.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|33.3|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||29.4|37.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|8.9|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||5.7|13.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.2|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.8|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.7|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.6|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.5|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||12.3|21.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.6|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.9|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|18.0|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|19.4|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|19.6|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|20.1|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2010-2012|||17.6|22.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|21.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|22.6|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2010 mid-year population estimates||||15.7|31.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|23.0|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|23.4|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.3|31.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|26.5|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||22.7|30.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|53.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||47.2|59.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.0|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.1|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.6|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.6|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.7|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.9|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.8|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.6|14.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.9|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||8.9|14.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|12.7|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|13.6|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.4|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.7|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.8|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.1|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.7|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistics Report, Deaths Final Data, 2011, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|16.0|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|16.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|16.2|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|16.2|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|17.4|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|17.8|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.8|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|20.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||18.6|23.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|21.8|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.8|25.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|28.7|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|16.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|7.3|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|7.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|8.0|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|10.3|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|12.0|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|13.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|13.8|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|15.7|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|15.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|16.5|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||9.7|23.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|22.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.8|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|6.0|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.9|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|13.1|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|13.5|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|14.6|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|14.7|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|15.6|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|15.8|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|15.8|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|17.4|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|19.2|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.3|28.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|19.6|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||12.3|26.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|19.7|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|24.3|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|28.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|33.6|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|35.6|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.2|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.3|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|10.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|10.9|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|11.7|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|11.9|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|12.6|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|17.1|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|17.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||11.8|22.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|18.4|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|18.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|19.1|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|26.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|0.0|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|3.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|7.6|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||0.0|18.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|15.2|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|7.7|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.2|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.2|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.4|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|10.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.0|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.4|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||8.9|14.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.5|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|12.5|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.9|15.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|12.5|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.1|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.2|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|16.5|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.7|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.9|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|18.5|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|20.3|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|22.2|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||19.5|25.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|23.2|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.5|28.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|24.3|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|30.9|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.8|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|7.2|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|7.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|7.6|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|7.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|9.0|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|10.3|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||7.7|13.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|11.0|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||7.9|14.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|11.5|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|11.7|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|12.5|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.0|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.3|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.4|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|13.5|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|14.3|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|14.5|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|14.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|15.2|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|15.5|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|15.8|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|17.0|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.8|22.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|18.4|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||15.6|21.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|23.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.1|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|12.2|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|12.3|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.0|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||9.0|18.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.1|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.0|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.4|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.9|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|15.7|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.3|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.7|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|19.1|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|19.3|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|19.8|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|20.2|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|20.5|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|21.5|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|24.0|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||20.5|27.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|25.7|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|27.7|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.0|35.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|37.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.1|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.4|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.9|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.8|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.9|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.4|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.2|14.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.6|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.1|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.1|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.4|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.9|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.9|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.9|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.4|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistcis Report Volume 63, Number 9. Table 16. Age-adjusted death rates for 113 selected ccauses by raqce and sex: United States 2012. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.5|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|15.6|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|16.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|17.3|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|18.3|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.2|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.2|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.2|24.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|22.0|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||19.7|24.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|24.8|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|25.2|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|32.2|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates||||23.9|42.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|13.1|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistcis Report Volume 63, Number 9. Table 16. Age-adjusted death rates for 113 selected ccauses by raqce and sex: United States 2012. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||American Indian alone||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|25.4|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|American Indian alone||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|6.0|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|8.2|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|8.8|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|9.7|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|13.9|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|14.0|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistcis Report Volume 63, Number 9. Table 16. Age-adjusted death rates for 113 selected ccauses by raqce and sex: United States 2012. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|14.6|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|14.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates||||8.3|24.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|17.1|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|17.6|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|20.7|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||11.9|29.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|23.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.3|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.0|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.0|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.7|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.9|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|11.7|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|11.9|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates||||6.5|19.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|16.1|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistcis Report Volume 63, Number 9. Table 16. Age-adjusted death rates for 113 selected ccauses by raqce and sex: United States 2012. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|17.0|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|17.4|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|20.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|22.9|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|24.8|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|28.6|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|31.4|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|33.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||24.1|43.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.7|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.1|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.7|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|8.9|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|9.2|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|9.9|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|11.2|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|12.0|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistcis Report Volume 63, Number 9.  Table 17. Age-adjusted death rates for 113 selected causes by Hispanic origin http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|12.9|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|16.9|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|17.6|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|17.6|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||11.6|23.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|22.7|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|24.2|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|24.9|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|3.4|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|22.1|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.8|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|8.9|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||0.0|21.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|13.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|167.0|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.4|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.6|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.8|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||5.7|10.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.8|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|8.9|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.0|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.1|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|11.5|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|11.6|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.1|14.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.0|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates||||6.8|19.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.1|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.7|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|14.2|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|14.4|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Vital Statistcis Report Volume 63, Number 9. Table 16. Age-adjusted death rates for 113 selected ccauses by raqce and sex: United States 2012. http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|14.6|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|14.8|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.1|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.6|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.3|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.3|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.8|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|19.7|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|21.9|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.4|27.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|22.1|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||19.4|24.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|25.2|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|34.0|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.5|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.9|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||3.9|8.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.7|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|7.0|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|7.9|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|8.7|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|8.7|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates||||5.5|13.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|8.9|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|9.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|10.3|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.1|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.3|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||8.5|14.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.3|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|11.6|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|12.2|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|12.3|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|14.5|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|15.1|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|15.5|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|16.4|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|16.9|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.6|22.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|20.2|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||17.3|23.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|20.7|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|27.9|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.8|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||6.3|14.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.5|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.|ICD-10 codes J10-J18; (2010 includes ICD-10 code J09) as underlying cause of death||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.0|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.6|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.1|Cleveland, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Cleveland Department of Public Health Office of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.5|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.8|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.9|Washington, DC|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|15.1|Houston, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|15.8|Los Angeles, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|17.2|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|17.4|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2011 mid-year population estimates||||11.5|25.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|19.6|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|19.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Tarrant County (not just Fort Worth)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|20.9|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.6|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|23.1|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|24.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||21.1|28.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|25.5|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.9|33.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.5|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|32.4|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.8|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.5|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.9|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||8.9|11.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.9|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||8.6|13.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.2|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.1|13.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.6|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.7|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.8|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||11.6|14.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|13.0|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||11.2|14.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|15.1|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates||||11.6|19.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|15.8|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|15.9|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.5|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||14.5|18.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|17.2|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|17.8|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|18.5|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.7|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||18.5|22.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|22.4|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|22.5|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.4|26.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|27.2|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|28.6|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|15.0|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|16.8|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|American Indian alone||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|27.9|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|70.5|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|6.5|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.1|9.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|6.7|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|9.4|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|13.8|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||11.8|16.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|15.0|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|16.2|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates||||9.1|26.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|19.5|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||11.7|27.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|19.6|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|23.7|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|5.5|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.6|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|13.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||8.6|21.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.4|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.8|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.9|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||9.8|22.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|16.2|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|16.3|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||13.2|19.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|17.1|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|18.0|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|19.1|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates||||12.1|28.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|19.5|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|21.6|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|21.7|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|22.6|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.9|32.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|25.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|26.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||17.7|34.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|28.8|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.3|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.4|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.6|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.1|12.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.9|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.9|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|11.4|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||7.8|16.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|13.2|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|14.0|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||6.1|22.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|17.0|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|17.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||11.3|23.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|18.4|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|24.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|15.4|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|22.1|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|0.5|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|8.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||0.0|18.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|10.1|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|263.4|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.1|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.7|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|9.7|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|10.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|10.6|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||9.3|12.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|11.1|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||9.4|13.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|11.6|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||9.0|14.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.2|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates||||8.0|20.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.6|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||11.4|15.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|14.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.8|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||13.2|18.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.9|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.9|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|17.9|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.0|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|20.1|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|21.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||18.6|23.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|22.2|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|23.0|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.4|28.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|23.9|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|28.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|41.4|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.8|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|7.6|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.0|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.0|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.7|9.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.5|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|10.0|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||8.5|11.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|11.1|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||8.1|14.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|11.3|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||8.5|14.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|12.1|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||9.8|14.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|12.3|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||10.1|14.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|12.9|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|13.7|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|13.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates||||9.5|19.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|14.0|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|14.9|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.0|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||11.0|20.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.1|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|15.1|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||12.5|17.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|17.4|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|18.7|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|21.1|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|28.0|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|9.0|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|9.2|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.5|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||7.8|16.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.8|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Pneumonia & Influenza Mortality Rate ICD-10 codes: J10-J18, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|12.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||10.8|14.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.6|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||11.6|18.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.3|San Francisco, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Value is reported for a multi-year period, 2013-2015|||15.0|19.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.4|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.5|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2012 mid-year population estimates||||11.6|25.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.8|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|18.6|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.9|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|20.5|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|22.4|Chicago, Il|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|22.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||19.1|26.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.0|San Jose, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|27.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||24.1|31.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|28.6|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|28.7|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|36.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|37.2|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||28.9|47.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.6|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.3|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.7|9.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.0|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||6.9|11.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.2|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.3|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||8.7|11.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|12.5|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||10.2|15.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|13.1|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|14.2|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|14.4|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||12.6|16.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|15.1|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|15.5|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|17.3|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics|Pneumonia and influenza deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18||||13.5|21.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|22.7|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.6|26.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|23.9|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|24.0|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|29.3|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|30.2|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||27.6|32.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|42.5|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||39.4|45.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|0.0|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|15.1|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|21.0|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|American Indian alone||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|5.1|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|6.1|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||2.2|15.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|8.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|9.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.8|13.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|12.9|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|15.2|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|15.2|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics|Pneumonia and influenza deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Representative of Asiain population alone. Does not include Pacific Islander population|||8.9|24.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|15.9|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|18.8|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|23.0|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||14.7|31.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|10.9|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.7|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|12.8|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||10.1|15.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|14.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|16.3|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|18.7|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics|Pneumonia and influenza deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18||||11.7|28.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|20.9|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.0|30.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|21.6|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|23.2|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|25.3|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|25.5|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|28.5|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|47.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||35.7|58.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|54.6|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||41.7|70.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|1.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.1|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.4|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.9|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.6|11.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.0|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||5.1|14.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|11.6|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|12.8|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|19.5|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||10.2|28.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|20.8|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|22.0|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|30.9|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||22.8|38.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|44.7|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||39.9|49.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|7.6|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|3.0|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|6.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||0.0|13.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.0|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.9|10.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.0|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.5|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.6|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.4|9.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.0|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||6.8|11.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.7|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.5|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||8.6|12.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.2|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||9.6|15.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.6|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.1|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.7|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016).||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|18.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics|Pneumonia and influenza deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18||||12.3|27.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|23.7|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||19.0|29.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|24.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|29.6|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||26.5|32.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|38.5|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||33.9|43.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|39.5|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.1|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.4|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.9|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||4.6|9.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.5|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.7|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||6.0|9.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.5|9.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|10.4|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|10.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||8.9|12.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|11.8|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|13.2|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|13.4|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics|Pneumonia and influenza deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18||||9.3|18.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|14.3|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.2|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.8|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|19.9|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||15.3|25.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|22.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||19.6|25.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|30.1|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|32.6|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||29.0|36.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|9.1|Long Beach, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|9.2|Miami (Miami-Dade County), FL|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.1|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||8.4|11.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|12.7|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder: J10-J18|||||8.9|17.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|13.7|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||11.0|17.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|16.3|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.2|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.8|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Influenza and Pneumonia cause of death, J09-J18 (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.9|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||13.7|23.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|18.0|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|18.1|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||16.4|23.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|22.3|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County Vital Statistics|Pneumonia and influenza deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18||||15.6|30.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|27.6|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.8|35.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|28.0|Phoenix, AZ|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Final Year End Death Data|Deaths were considered a case if the keywords pneumonia and influenza were listed as the Cause of Death.  Cases were excluded if the Cause of Death indicated aspiration pneumonia.|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|30.1|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|30.8|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes J10 - J18|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|39.7|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||35.2|44.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|55.8|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||50.2|61.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|9.5|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||8.5|10.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|9.7|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||7.7|12.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.1|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||10.3|13.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.1|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18|||||10.1|15.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.6|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.7|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates||||10.4|17.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.8|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|14.4|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||12.6|16.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|15.2|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|16.4|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|21.3|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|22.2|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|24.1|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||19.8|28.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|27.3|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||24.8|29.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|42.8|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||39.6|45.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|12.5|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|31.0|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|4.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|8.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.0|12.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|10.1|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||5.0|20.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|12.9|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates||||7.1|21.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|13.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|14.0|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|18.5|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|27.2|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||17.8|36.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|10.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|11.1|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||3.5|27.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|12.0|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||9.4|14.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|15.1|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|15.9|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|16.1|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|17.0|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||10.9|25.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|19.1|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates||||12.2|28.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|20.3|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|22.0|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|23.9|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|25.8|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.8|36.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|32.6|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||23.0|42.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|51.2|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||38.6|66.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|0.0|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|9.5|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.1|12.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|9.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||5.6|16.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|11.3|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|11.4|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|12.7|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|17.7|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||9.3|26.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|17.9|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||11.8|24.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|20.9|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|41.3|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||36.8|45.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|6.8|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||0.0|16.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|6.9|Seattle, WA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.9|10.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|9.4|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||7.2|12.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|9.4|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||8.2|10.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|9.7|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates||||5.4|15.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|11.6|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||9.6|13.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|12.1|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18|||||9.6|15.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.4|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|14.2|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||11.8|16.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|15.1|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|15.4|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|16.3|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|22.5|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|24.8|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.0|30.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|25.5|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||22.6|28.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|43.6|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||38.8|48.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|8.3|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.0|9.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|9.3|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|9.9|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||8.0|12.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|11.4|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|11.8|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18|||||8.8|15.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|12.3|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates||||8.5|17.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|12.9|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||10.7|15.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|13.5|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|15.7|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|16.3|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|16.3|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|17.1|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.9|22.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|18.6|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|20.6|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||17.7|23.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|29.3|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||25.9|32.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|9.7|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|11.2|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||9.5|12.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|13.8|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder J09-J18|||||9.7|19.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|15.1|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2015 mid-year population estimates||||9.9|22.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|15.5|Fort Worth (Tarrant County), TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|National Center for Health Statistics|||||12.5|19.1
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|16.7|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||13.5|19.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|17.4|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|17.6|Boston, MA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|17.7|U.S. Total, U.S. Total|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||In 2007, NCHS introduced the category J08 for coding avian influenza virus. In 2009, the title for the ICD-10 code J09 was changed from Influenza due to identified avian influenza virus to influenza due to a certain identified influenza virus. This change was made to accommodate deaths from influenza A (H1N1) in the ICD-10 code J09 for data years 2009 and beyond.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|27.3|New York City, NY|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|NYC DOHMH Bureau of Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|28.2|Detroit, MI|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|34.8|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||27.1|44.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|36.0|Las Vegas (Clark County), NV|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Nevada Vital Records - Clark County Deaths|||||31.7|40.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|61.5|San Antonio, TX|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Bexar County level data|||55.7|67.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|9.8|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J19|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||8.8|10.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|10.1|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder|||||8.0|12.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|10.8|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||8.8|13.3
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|11.1|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|12.0|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|12.1|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates||||9.1|15.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|13.8|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|15.4|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||13.5|17.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|21.2|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.2|25.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|47.2|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|7.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|9.4|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates||||4.9|16.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|10.1|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.3|13.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|26.6|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|13.5|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|14.7|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||11.8|17.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|15.2|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|17.4|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates||||10.8|26.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|22.1|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.9|31.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|24.1|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|0.0|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|8.4|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.3|11.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|14.8|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||6.7|22.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|16.9|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|27.6|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|9.4|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|9.7|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||8.5|11.0
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|9.8|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates||||5.4|16.5
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|10.0|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||7.7|12.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|10.0|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder|||||7.8|12.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|10.4|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|10.5|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|14.8|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||12.3|17.4
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|20.4|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.1|25.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|7.9|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|8.4|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.1|9.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|9.1|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder|||||6.6|12.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|10.3|Kansas City, MO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|11.0|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|11.6|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates||||7.7|16.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|12.6|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||10.5|14.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|18.7|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.1|24.2
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|11.5|Portland (Multnomah County), OR|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|CDC Wonder|||||8.0|15.9
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|11.8|San Diego County, CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||10.1|13.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|11.9|Denver, CO|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Mortality data from the Colorado Department of Public Health and Environment||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|12.5|Oakland (Alameda County), CA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Alameda County vital statistics files|Using 2016 mid-year population estimates||||8.0|18.6
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|15.0|Indianapolis (Marion County), IN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|MCPHD Death Certificate data|||||11.2|19.7
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|19.3|Philadelphia, PA|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J10-J18|PA Eddie-->Vital Statistics|||||15.8|22.8
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|23.6|Minneapolis, MN|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18|Minnesota Vital Statistics||||||
Infectious Disease|Pneumonia and Influenza Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|26.8|Columbus, OH|Pneumonia and influenza deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.  ICD-10 codes: J09-J18.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||20.1|35.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|3.8|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|4.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|4.8|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.8|5.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|5.0|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2016||Bexar County level data|||3.9|6.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|6.1|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|6.4|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|6.8|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|7.2|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|8.5|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.1|10.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|8.5|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|8.9|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|9.3|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|11.1|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|All|12.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||10.8|14.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|15.1|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2017||Bexar County level data|||3.9|26.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|21.4|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|24.9|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|28.5|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||23.4|34.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|29.4|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|29.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|29.6|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|35.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|37.3|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI|38.2|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|7.4|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|7.8|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|8.1|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2018||Bexar County level data|||3.1|13.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|8.7|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|8.9|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|10.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|12.7|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||8.0|19.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|15.2|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||9.6|20.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|16.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black|38.7|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Black||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|4.2|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|4.3|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|4.9|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|5.4|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|6.0|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2019||Bexar County level data|||4.5|7.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|7.7|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|8.5|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|9.7|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||6.6|13.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|10.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic|11.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Hispanic||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because there are too few cases to protect confidentiality.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other|0.0|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2020||Bexar County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other|2.6|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other|5.8|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|Other||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because there are too few cases to protect confidentiality.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|0.6|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|0.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|1.5|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2021||Bexar County level data|||0.5|2.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|1.8|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|1.8|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|3.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|3.1|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||1.7|5.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White|3.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Both|White||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because there are too few cases to protect confidentiality.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|2.7|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.lung.org/finding-cures/our-research/trend-reports/TB-Trend-Report.pdf||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|3.4|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|3.5|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.3|4.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|4.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|4.7|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|5.5|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|5.7|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|5.8|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|6.1|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|7.0|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||4.0|10.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|8.1|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|9.8|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||7.6|12.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Female|All|9.8|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|3.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|4.5|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.lung.org/finding-cures/our-research/trend-reports/TB-Trend-Report.pdf||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|4.6|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|6.0|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||4.5|7.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|6.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2023||Bexar County level data|||4.7|8.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|7.7|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|7.7|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|8.0|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Seattle-King County Surveillance||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|8.8|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|8.8|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|10.1|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|||||6.4|13.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|10.8|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|11.2|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|15.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||12.8|18.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2010|Male|All|16.4|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|3.5|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|3.5|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|3.6|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.4|5.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|4.5|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|4.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.7|5.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|5.2|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||3.3|7.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|5.5|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|6.3|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|6.3|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|6.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|7.1|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|8.3|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|8.4|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|8.7|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|8.7|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|8.9|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|9.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||7.8|11.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|All|10.8|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|5.8|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|American Indian/Alaska Native|54.7|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|3.9|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|18.0|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||11.8|24.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|19.6|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|20.2|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||Does not include Pacific Islander||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|23.6|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|23.7|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|24.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||19.9|30.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|25.2|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|26.0|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||14.2|43.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|28.0|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|28.5|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|30.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|30.5|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|41.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI|47.3|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|3.4|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|5.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|6.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|6.3|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|6.6|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|6.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.0|10.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|7.0|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|7.5|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||4.0|12.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|7.8|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|7.9|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|8.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|12.5|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|13.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|14.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|14.4|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|14.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black|23.5|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Black||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|3.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|4.1|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|4.4|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|4.5|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||2.5|7.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|4.7|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|4.7|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|4.8|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|5.5|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.5|7.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|5.8|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|6.9|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|7.4|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|7.5|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|8.0|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|9.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|11.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|12.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic|14.6|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other|0.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other|3.8|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other|16.1|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||Native Hawaiian or other PI||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other|19.6|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other|21.7|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|0.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|0.5|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|0.6|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|0.8|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|0.8|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|1.4|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.6|2.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|1.5|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|1.5|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|1.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|1.6|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|2.5|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|2.5|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|3.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|3.2|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White|3.9|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Both|White||Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|2.1|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|2.5|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|2.6|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.lung.org/finding-cures/our-research/trend-reports/TB-Trend-Report.pdf||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|3.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|3.3|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|3.9|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|4.0|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|4.2|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.4|6.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|4.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|4.4|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.1|5.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|4.7|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|4.7|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|6.0|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|6.7|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|7.1|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||5.3|9.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|7.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|7.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|10.1|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All|11.2|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Female|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|3.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|3.7|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|4.2|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.lung.org/finding-cures/our-research/trend-reports/TB-Trend-Report.pdf||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|4.7|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|4.8|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|4.8|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.4|6.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|5.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|7.3|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|7.8|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|7.9|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|7.9|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|9.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|9.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|10.0|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|10.1|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|11.4|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|11.7|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||9.3|14.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|14.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All|16.6|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2011|Male|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|1.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|3.2|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.cdc.gov/tb/statistics/tbcases.htm||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|3.5|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|3.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.9|4.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|4.0|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|4.1|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|4.9|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|5.0|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||3.2|6.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|5.2|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|5.4|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|5.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|5.9|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|6.6|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|7.4|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|7.4|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|7.5|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|7.5|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|7.5|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|7.8|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|8.3|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|9.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|9.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||8.0|11.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|10.7|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|10.8|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All|14.1|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|0.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|6.3|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|9.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|American Indian/Alaska Native|109.2|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|3.6|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|17.5|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||11.3|23.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|19.0|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||Does not include Pacific Islander||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|20.9|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|22.0|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|23.1|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||18.6|28.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|23.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|24.0|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|25.2|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|26.4|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|28.5|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|28.5|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|29.4|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|29.6|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|35.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI|61.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|1.7|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|2.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.3|4.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|2.7|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|2.9|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|3.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|4.2|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|4.9|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|5.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|5.8|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|7.2|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|7.3|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|8.0|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|8.1|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|8.5|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|8.5|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|9.9|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||5.7|15.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|10.2|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|11.6|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||6.7|16.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|12.6|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|12.7|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|15.3|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|16.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|32.5|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black|50.0|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|2.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|3.1|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|3.9|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.3|5.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|4.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|4.4|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|4.5|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|4.6|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|4.7|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|4.8|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|5.1|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|5.3|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||3.1|8.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|5.3|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|5.8|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|6.0|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|6.2|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|7.3|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|7.4|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|8.0|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|9.4|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|12.5|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic|12.5|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other|2.4|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other|3.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other|4.9|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other|12.1|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||Native Hawaiian or other PI||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other|13.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other|17.4|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|0.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|0.7|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|0.8|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|0.9|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.0|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.3|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.5|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.8|2.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.6|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.8|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|1.8|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|2.1|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|2.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||1.2|4.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|3.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|3.3|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|3.5|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|4.4|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White|10.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Both|White||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|2.6|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|2.7|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|3.0|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||1.9|4.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|3.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|3.8|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|4.0|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|4.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|4.7|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|5.3|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|5.5|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|5.9|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|6.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|6.5|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|6.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|8.4|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|8.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||6.6|11.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|10.2|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All|12.4|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|2.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|4.0|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.2|6.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|4.5|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.2|5.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|4.7|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|5.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|5.5|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|5.6|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|5.9|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|6.2|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|6.3|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|6.5|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|7.2|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|7.3|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|8.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|9.4|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|9.5|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|9.8|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|10.2|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|10.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||8.3|13.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|11.4|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|11.6|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|13.0|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All|15.9|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2012|Male|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|3.2|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.cdc.gov/tb/statistics/tbcases.htm||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|3.5|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|3.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.7|4.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|3.7|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|3.9|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|3.9|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|4.0|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.8|5.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|4.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|5.1|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|5.3|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|5.6|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|5.7|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|5.8|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|6.0|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||4.0|7.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|6.4|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|6.4|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|6.6|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|7.1|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|7.3|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|7.7|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|8.0|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||6.6|9.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|8.1|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|8.2|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|8.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|12.3|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All|13.0|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|0.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|5.4|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|American Indian/Alaska Native|6.2|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|4.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|9.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|11.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|13.5|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||8.1|18.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|17.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|18.7|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||Does not include Pacific Islander||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|20.8|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||16.6|25.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|21.4|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|21.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|25.3|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|26.1|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|27.7|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|29.4|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|29.7|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|30.4|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|33.2|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI|105.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|1.9|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|3.0|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|3.9|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|4.9|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|5.0|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|5.1|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.0|8.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|5.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|5.4|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|5.8|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|6.2|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|6.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||3.2|11.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|8.1|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|8.4|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|8.5|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|8.8|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|10.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|10.5|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||5.9|15.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|11.0|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|12.2|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|13.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|18.6|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black|21.8|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Black||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|2.6|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|3.8|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|4.2|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.5|5.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|4.3|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|4.3|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||2.4|7.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|4.4|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|4.5|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|4.9|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|5.0|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|5.1|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|5.9|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|6.4|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|7.4|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|7.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|10.0|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|10.0|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|10.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic|11.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic||Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other|0.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other|6.8|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other|11.3|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||Native Hawaiian or other PI||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other|18.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other|21.5|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|0.4|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|0.7|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 2. Tuberculosis Cases, Percentages, and Case Rates per 100,000 Population by Hispanic Ethnicity and non-Hispanic Race: United States, 1993-2013,  Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|0.8|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.1|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.2|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.2|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.5|1.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.2|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.3|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.5|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.6|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|1.8|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|2.0|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||1.0|3.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|2.2|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|2.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|3.6|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|3.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|4.1|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White|11.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Both|White||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|1.7|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|2.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|2.3|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 17 Tuberculosis Cases and Rates per 100,000 Population by Hispanic Ethnicity and Non-Hispanic Race, Sex, and Age Group: United States, 2013, Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|2.9|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|3.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|3.4|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.2|4.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|3.5|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|3.7|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.1|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.4|6.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.3|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.4|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.6|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.7|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.8|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|4.9|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|5.4|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|5.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||4.0|7.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|6.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|6.7|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|8.1|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|10.4|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All|10.8|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|3.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|3.7|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Table 17 Tuberculosis Cases and Rates per 100,000 Population by Hispanic Ethnicity and Non-Hispanic Race, Sex, and Age Group: United States, 2013, Reported Tuberculosis in the United States||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|3.8|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.6|5.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|4.0|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.2|6.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|4.0|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|TB Program, Public Health - Seattle & King County||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|4.7|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|5.5|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|5.5|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|5.8|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|5.8|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|5.9|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|5.9|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|DSHS, 2015||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|7.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|7.7|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|7.8|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||4.6|11.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|8.1|Washington, DC|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|8.3|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|8.4|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|9.4|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|10.0|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|10.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||8.1|13.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|11.3|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Dallas County Health and Human Services||All data are county level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|11.4|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|14.1|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All|15.1|San Francisco, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2013|Male|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|2.2|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|3.0|U.S. Total, U.S. Total|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|http://www.cdc.gov/tb/statistics/tbcases.htm||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|3.3|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.2|4.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|3.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.7|4.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|3.7|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|3.8|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|3.9|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|4.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|4.8|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2024||Bexar County level data|||3.8|5.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|5.0|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|5.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|5.2|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|5.7|Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data|||||4.3|7.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|5.8|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||3.9|7.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.5|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.8|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.8|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.8|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|6.9|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|7.3|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|7.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||6.1|9.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|All|9.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|0.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|American Indian/Alaska Native|9.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||American Indian alone||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|8.7|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|11.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|17.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||13.6|21.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|19.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|19.8|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2025||Bexar County level data|||8.1|31.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|20.1|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|20.4|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|21.3|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|21.4|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||14.6|28.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|23.7|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|25.7|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|26.8|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|27.1|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|33.2|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|34.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI|52.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|1.7|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|3.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|3.6|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2026||Bexar County level data|||0.5|6.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|3.7|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|5.0|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|5.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|5.5|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|6.0|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||2.9|11.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|6.2|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.7|9.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|6.7|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|9.0|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|9.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|10.1|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|10.3|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|17.1|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|22.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black|25.7|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|1.9|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.8|3.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|3.4|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|4.2|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|4.3|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||2.4|7.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|4.4|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|5.1|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|5.2|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|5.3|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|5.9|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|5.9|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2027||Bexar County level data|||4.5|7.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|6.1|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|6.2|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|10.0|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|10.8|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic|10.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic||Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Multiracial|2.7|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other|0.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other|4.8|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other|21.4|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2028||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|Other||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|0.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|0.4|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|0.7|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|0.8|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|0.9|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.3|1.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|0.9|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.0|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.3|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.3|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.3|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2029||Bexar County level data|||0.3|2.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.3|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.5|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|1.6|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|2.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|2.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||1.2|4.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White|9.2|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|1.3|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|2.6|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.4|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.4|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2030||Bexar County level data|||2.2|4.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.4|4.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.6|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|3.7|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|4.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|4.2|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|4.7|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|4.7|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|4.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|5.1|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|6.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||4.7|8.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|6.8|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|7.1|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|8.5|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All|10.6|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|3.1|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|3.5|Chicago, Il|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|3.6|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.4|4.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|4.1|Baltimore, MD|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Baltimore City Health Department TB Elimination Program||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|4.1|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|4.7|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|HIV/AIDS, Sexually Transmitted Disease & Tuberculosis case reports, Other Communicable Disease case reports, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- tb query|confidence intervals calculated using clopper-pearson method|||2.8|7.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|5.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Colorado Department of Public Health and EnvironmentDisease Control and Environmental Epidemiology DivisionTuberculosis Program, RVCT data||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|5.2|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Tarrant County (not just Fort Worth)||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|5.4|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MEDSIS communicable disease database||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|6.2|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2031||Bexar County level data|||4.6|7.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|6.5|Miami (Miami-Dade County), FL|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|6.8|Minneapolis, MN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Minnesota Department of Health, TB Control Program (case counts)|2010 US Census|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|6.9|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|7.9|Long Beach, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Source:  California Department of Public Health, California Tuberculosis Data Tables, 2014.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|8.1|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|8.6|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||6.5|11.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|8.9|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|9.4|Los Angeles, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Sources: (1) RVCT official data for confirmed cases for years 2012-2014, (2) TRIMS data from incidence and patient tables for cases confirmed in years 2012-2014; (3) Population Denominators from OHAE prepared by Hedderson Demographic Services for LA County Internal Services Department.|Countywide data only.|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|10.7|San Jose, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), 2012-2014; U.S. Census Bureau, Census 2010||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All|13.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2014|Male|All||Indianapolis (Marion County), IN|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|MCPHD Infectious Disease Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|2.4|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|(500 Cities Project)||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|2.5|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|2.8|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|3.1|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|3.3|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query|||||2.1|4.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|3.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.8|4.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|4.1|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|4.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2032||Bexar County level data|||3.4|5.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|4.5|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||2.8|6.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|4.7|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|5.8|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|6.8|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|7.2|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|7.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|All|10.8|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|13.8|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2033||Bexar County level data|||4.2|23.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|16.3|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||10.4|22.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|17.8|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|21.4|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|21.8|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query|||||11.9|36.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|22.1|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|23.3|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|23.5|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||19.1|28.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|26.0|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|29.4|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|29.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|30.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI|38.8|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|2.2|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|2.2|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|(500 Cities Project)||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|4.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|6.3|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|6.5|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|6.8|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|8.6|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|9.2|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||5.0|13.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|9.8|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||5.6|15.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|11.9|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|12.3|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black|15.9|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black||San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2034||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Black||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|2.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|3.1|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|4.0|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||2.2|6.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|4.8|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|5.1|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|5.4|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2035||Bexar County level data|||4.0|6.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|5.7|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|11.5|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic|12.8|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic||Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other|0.0|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2036||Bexar County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other|3.8|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|Other||Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|0.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|0.0|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|(500 Cities Project)||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|0.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|0.9|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||0.3|1.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|1.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|1.3|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|1.4|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|1.8|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2037||Bexar County level data|||0.7|2.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|2.6|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|3.1|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White|9.2|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White||Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White||Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here because number is too small for rate calculation.; Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Both|White||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|1.6|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|1.8|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|2.4|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|2.5|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|(500 Cities Project)||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|2.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|3.1|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2038||Bexar County level data|||2.0|4.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|3.3|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|3.5|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query|||||1.9|5.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|3.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.5|4.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|4.2|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|4.7|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|4.8|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|5.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|5.3|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|7.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All|9.1|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||7.1|11.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Female|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|2.2|Kansas City, MO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|(500 Cities Project)||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|3.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|3.2|Phoenix, AZ|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National TB Surveillance System||Maricopa County level data||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|3.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||2.5|4.9
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|4.5|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|4.7|Detroit, MI|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|4.8|Fort Worth (Tarrant County), TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Tarrant County Public Health||Value is for all of Tarrant County.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|5.7|San Antonio, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Texas TB File for Bexar County; Denominators: CDC Wonder; Bridged Pop Estimates 1990-2039||Bexar County level data|||4.2|7.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|6.3|Seattle, WA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|8.4|New York City, NY|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|NYC DOHMH Bureau of Tuberculosis Control||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|9.6|Boston, MA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Infectious Disease Bureau, Boston Public Health Commission||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|9.9|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||7.7|12.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|10.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All|14.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2015|Male|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|2.4|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||1.7|3.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|3.3|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query|||||2.1|4.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|3.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|4.9|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|5.7|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||3.8|7.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|7.8|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|7.8|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|9.0|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|All|9.0|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||7.6|10.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|18.4|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query|||||9.5|32.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|22.5|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||18.4|27.4
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|24.5|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|27.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|30.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|31.1|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI|32.9|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black|4.4|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black|6.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||3.0|10.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black|11.0|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||6.3|15.8
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black|11.2|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black|11.5|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black|12.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|2.3|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||1.0|3.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|4.7|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|4.8|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|5.3|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||3.2|8.3
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|7.6|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|9.2|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic|10.4|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Other||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Other||Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|Other||San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable.; Confirmed cases only.; Cases include non-residents and those of unknown residence who received diagnosis while in the county.; Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White|0.6|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White|1.1|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White|1.1|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White|1.7|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White|2.4|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Both|White||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|2.3|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|2.7|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||1.7|3.7
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|3.6|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|5.2|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|5.3|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|5.9|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All|6.4|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||4.8|8.5
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All||Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|2.1|Las Vegas (Clark County), NV|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.||||||1.2|3.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|4.0|Portland (Multnomah County), OR|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|OPHAT TB query|||||2.3|6.6
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|5.0|Denver, CO|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|National Tuberculosis Surveillance System|Cases reported by count date|||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|6.3|Philadelphia, PA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|7.1|Columbus, OH|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Ohio Disease Reporting System|2011-2015 (Combined) Population Estimate||||4.1|10.2
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|9.7|San Diego County, CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|10.5|Dallas, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|11.7|Oakland (Alameda County), CA|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|Reports of Verified Cases of Tuberculosis|Denominator used was CA Dept of Finance projections year of interest|Data is for Alameda County (excluding the city of Berkeley. |||9.3|14.1
Infectious Disease|Tuberculosis Incidence Rate (Per 100,000 people)|2016|Male|All|12.7|Houston, TX|Tuberculosis incidence - crude rate per 100,000 population using 2010 US Census figures.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|All|1.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.0|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|All|2.4|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|All|2.6|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|All|4.7|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||4.5|5.0
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|All|58.3|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Asian/PI|0.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.0|0.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Asian/PI|0.3|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||0.0|0.6
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Asian/PI||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Black|3.1|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||2.4|3.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Black|10.3|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Black|12.2|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||11.5|12.9
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Black|143.0|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Hispanic|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.7|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Hispanic|2.5|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||2.1|2.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Hispanic|2.8|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Other|0.8|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.2|1.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|Other||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|White|0.3|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||0.2|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|White|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.8|1.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|White|14.5|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Both|White||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Female|All|0.3|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.2|0.4
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Female|All|0.5|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Female|All|0.9|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||0.8|1.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Female|All|1.3|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Female|All||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Male|All|2.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.7|2.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Male|All|4.3|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Male|All|5.0|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Male|All|8.6|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||8.1|9.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2010|Male|All|105.9|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|All|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|All|0.5|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|All|1.1|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.0|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|All|2.8|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|All|4.5|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||4.2|4.7
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|All|65.3|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|American Indian/Alaska Native|0.3|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||American Indian alone||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Asian/PI|0.2|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||0.0|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Asian/PI|0.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.0|0.4
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Asian/PI|0.5|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Asian/PI||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Asian/PI||San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Black|0.5|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Black|0.7|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Black|3.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||2.6|4.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Black|10.4|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Black|11.4|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||10.6|12.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Black|162.6|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Hispanic|0.3|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Hispanic|0.7|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Hispanic|0.9|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.7|1.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Hispanic|2.3|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||1.9|2.6
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Hispanic|2.7|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Other|0.1|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Other|0.7|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.1|1.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Other||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|Other||San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|White|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|White|0.4|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|White|0.6|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||0.4|0.7
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|White|0.9|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.7|1.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|White|14.9|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Both|White||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All|0.1|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All|0.2|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All|0.3|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.2|0.4
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All|0.6|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All|1.0|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||0.9|1.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All|1.6|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Female|All||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|0.4|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|0.8|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|1.9|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.6|2.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|4.1|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|5.7|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|8.0|Chicago, Il|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Illinois Department of Public Health Hospital Discharge Data|Analyzed by Chicago Department of Public Health||||7.6|8.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2011|Male|All|117.7|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|0.3|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|0.4|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|0.9|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|1.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.0|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|1.6|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|2.1|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|2.6|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|2.7|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/12|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|7.2|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2012 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||6.4|8.0
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|All|52.2|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|American Indian/Alaska Native|0.3|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||American Indian alone||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Asian/PI|0.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.0|0.4
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Asian/PI|0.2|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Asian/PI|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Asian/PI|1.8|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2012 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||1.0|2.9
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Asian/PI||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|1.2|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|1.4|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|3.9|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||3.1|4.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|5.9|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|8.1|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|12.1|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/14|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|20.2|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2012 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||17.4|23.0
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Black|125.4|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|0.3|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|0.4|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|0.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|0.7|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.5|0.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|2.2|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/15|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|2.7|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Hispanic|4.0|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2012 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||2.9|5.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Other|1.1|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.4|1.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Other||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|Other||San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|0.2|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|0.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|0.9|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/16|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.8|1.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|1.2|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2012 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||0.6|2.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White|13.0|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Both|White||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.0|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.1|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.2|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.2|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.3|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.5|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|0.6|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/17|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|1.6|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All|1.7|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2012 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||1.2|2.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Female|All||Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is supressed here because minimum threshold to share was not met.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|0.6|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|0.6|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|1.5|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|2.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.7|2.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|2.3|Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|2.7|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|3.4|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|4.1|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|4.7|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/18|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|4.7|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2012|Male|All|90.3|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|0.6|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|0.7|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|1.0|Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|1.3|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.1|1.4
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|1.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|2.2|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|2.3|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|2.7|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|6.7|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||5.9|7.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|All|44.9|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Asian/PI|0.3|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/20|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Asian/PI|0.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.1|0.7
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Asian/PI||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Asian/PI||San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|0.8|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|1.7|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|5.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|8.6|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|11.4|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/21|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|16.3|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||13.8|18.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Black|92.1|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|0.3|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|0.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.2|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|0.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|0.6|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|1.6|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|1.8|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/22|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Hispanic|5.7|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||4.4|7.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Other|0.6|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Other|2.8|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.6|4.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|Other||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|0.5|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|0.5|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|0.9|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/23|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.8|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|1.3|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||0.7|2.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White|14.1|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Both|White||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.1|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.2|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.2|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.3|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.2|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.4|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.5|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/24|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|0.6|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|1.3|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All|1.6|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||1.1|2.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Female|All||Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is supressed here because minimum threshold to share was not met.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|0.4|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|1.0|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|1.3|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|1.8|Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|2.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.9|2.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|2.6|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|4.0|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|4.1|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/25|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|4.2|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|4.7|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|12.0|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2013 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||10.5|13.6
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2013|Male|All|80.3|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|0.3|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|0.5|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|0.7|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|1.0|Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|These data are not de-duplicated; numerator is # of applicable visits, not number of residents.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|1.6|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||1.4|1.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|1.8|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|2.2|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/26|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|2.2|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|2.3|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|2.5|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|5.4|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||4.7|6.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|All|42.7|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Asian/PI|0.2|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.0|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Asian/PI|0.5|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/27|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Asian/PI||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Asian/PI||San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Black|1.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Black|1.3|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Black|9.4|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Black|10.0|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/28|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Black|16.5|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||13.9|19.0
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Black|90.6|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|0.3|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|0.6|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|0.6|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.8|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|1.9|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/29|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|2.1|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Hispanic|3.9|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||2.8|5.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Other|0.1|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Other|0.5|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Other|3.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||2.1|4.6
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|Other||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White|0.3|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White|0.8|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/30|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White|1.1|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.8|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White|12.0|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <11||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Both|White||Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|0.1|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|0.2|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|0.5|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||0.3|0.6
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|0.5|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/31|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|0.6|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|0.6|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|1.2|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All|1.6|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||1.1|2.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All||Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Female|All||Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is supressed here because minimum threshold to share was not met.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|0.5|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|0.9|Phoenix, AZ|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Maricopa County Hospital Dischages Data (Emergency Department Visits)||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|1.3|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|New York State Dept of Health: Statewide Planning and Research Cooperative System (SPARCS), updated Oct 2015|Numerator: Number of ED only (treat and release) visits discharged alive from NYC hospital among NYC residents, with external cause of injury code ICD-9-CM E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4; Denominator: DOHMH population estimates updated Oct 2015|Data analyzed by NYC Department of Health and Mental Hygiene. Rate per 10,000 and age-adjusted to US 2000 Standard Population.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|2.7|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County|||||2.4|3.1
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|3.2|Miami (Miami-Dade County), FL|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|3.8|Minneapolis, MN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Minnesota Hospital Association|Rates per 10,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|3.9|Los Angeles, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Office of Statewide Health Planning and Development, Hospital Discharge and Emergency Department Data. July 1,2010 Population Estimates, prepared by Hedderson Demographic Services for Los Angeles County Internal Services Department, released 9/30/32|The hospital discharge and ED data each include 5 e-code variables.  The five variables were searched for the first listed valid ecode other than E000-E030, E849, E967, E869.4, E870E879, or E930E949.  The ecodes listed in the BCHC Requested Methodology column were used to identify which records had a first listed valid ecode that was firearm-related. 2010 population was used as denominator for all years, rates were age adjusted to 2000 US population|These data are county level. The hospital discharge data set includes records for ED visits where the patient was admitted to the same facility where ED treatment was provided.  The emergency department data set includes all other ED records (treated and released, died, admitted to a different facility, etc.). The reported rates include all ED visits. Data is provided for the county rather than the city because the only geographical information below the county level in the data are zip codes, which do not correspond well to the city boundary.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|4.5|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Center for Health Information and Analysis||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|4.5|U.S. Total, U.S. Total|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NEISS data and census population figures as accessed via CDC WISQARS||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|9.3|Oakland (Alameda County), CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|OSHPD|Numerator = Firearm-Related ED visits (treated and released) from OSHPD, Denominator = 2014 population data from ESRI|First 5 positions were reviewed for the firearm related ICD-9-CM codes E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4..|||8.0|10.7
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2014|Male|All|76.2|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|All|0.4|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|All|0.8|Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|These data are not de-duplicated; numerator is # of applicable visits, not number of residents. These rates include ED visits through the end of September 2015, prior to the CMS mandated transition to ICD-10-CM.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|All|1.5|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||1.3|1.6
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|All|2.1|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|All|9.2|Portland (Multnomah County), OR|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||7.2|11.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|All|62.9|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Asian/PI|0.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||0.1|0.7
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Asian/PI||San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Black|1.4|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Black|5.8|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||4.7|6.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Black|8.7|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Black|143.9|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Hispanic|0.4|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Hispanic|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||0.8|1.3
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Hispanic|1.7|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|Other|1.5|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|White|0.2|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|White|1.0|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||0.8|1.2
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|White|8.3|Portland (Multnomah County), OR|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||6.2|10.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Both|White|19.1|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Female|All|0.1|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Female|All|0.4|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data                  ||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Female|All|0.4|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||0.3|0.5
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Female|All|2.0|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Female|All||Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is supressed here because minimum threshold to share was not met.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Male|All|0.6|San Diego County, CA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch; SANDAG, Current Population Estimates, 4/2017.|ICD-9 CM codes E985.0-E985.4, E970, E979.4 were excluded. Population used for rate is from San Diego Association of Governments. Adjusted rates are adjusted to 2000 U.S. Standard Population. ICD-9 CM codes were used for the months of January-September 2015. ICD-10 CM codes (W32-W33, X72-X74, X93-X95, W3400, W3409-W3410, W3419) were used for the months of October-December.|||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Male|All|1.3|Denver, CO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Colorado Hospital Association ED Discharge Data|Numerator: Number of visits by Denver residents discharged from any CO ED with a firearm-related diagnosis code in the first 5 diagnosis fields.|These data are not de-duplicated; numerator is # of applicable visits, not number of residents. These rates include ED visits through the end of September 2015, prior to the CMS mandated transition to ICD-10-CM.||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Male|All|2.5|Las Vegas (Clark County), NV|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Nevada Hospital Discharge Data - Clark County||ICD-9-CM switched to ICD-10-CM on Oct 1, 2015. Therefore the last quarter of 2015 was not included in the analysis.|||2.2|2.8
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Male|All|3.9|Boston, MA|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|Acute hospital case-mix databases, Massachusetts Department of Public Health|Population denominators based on extrapolation after year 2010|For calculation of rates, emergency department visits were identified among emergency department, observational stay, and inpatient hospitalizations discharge data||||
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Male|All|16.2|Portland (Multnomah County), OR|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||12.4|20.7
Injury/Violence|Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)|2015|Male|All|108.9|Kansas City, MO|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.2|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.8|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||4.9|6.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.1|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.2|Seattle, WA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.4|8.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.3|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.6|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.2|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||7.8|10.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|10.1|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|10.1|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|10.8|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.0|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.4|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||12.7|16.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|15.4|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||13.0|18.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|16.0|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.9|19.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|17.7|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||15.7|19.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|18.9|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates||||14.9|23.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|23.3|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|7.3|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|11.5|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|2.2|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||1.3|3.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|2.3|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|2.9|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|4.1|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||1.3|6.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|4.9|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|9.2|Seattle, WA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||3.7|21.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|3.3|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|4.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|14.8|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|16.0|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||10.5|21.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|17.8|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|18.4|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||11.7|27.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|18.5|Seattle, WA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||8.4|37.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|22.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|22.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|24.2|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|26.0|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|28.7|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||22.3|36.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|30.6|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||23.4|39.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|31.5|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|31.7|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||27.5|35.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|34.2|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|39.9|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||30.6|51.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|48.9|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|55.9|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates||||42.4|72.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.4|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.7|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.2|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.7|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.8|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||2.8|7.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|5.9|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.5|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||5.0|8.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.7|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.4|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.7|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||6.7|12.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|5.9|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.1|11.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.4|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||2.4|4.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.3|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.8|Seattle, WA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||3.0|7.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|6.0|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|8.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.2|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.5|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.9|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.9|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.0|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||7.6|13.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||7.7|12.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.6|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.5|14.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.8|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.1|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||8.5|14.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|0.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.1|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.1|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||0.6|1.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.5|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.6|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||1.1|3.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.1|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.5|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.0|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.3|Seattle, WA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||1.5|6.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.4|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.4|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||2.3|4.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||2.9|5.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.9|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.1|Seattle, WA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||6.1|13.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.6|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.8|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2010-2012|||9.0|13.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.1|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.3|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.7|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||12.9|18.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.1|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.1|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.8|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.9|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|19.1|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|24.9|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||21.8|28.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|27.6|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|27.9|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||23.1|33.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|28.3|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.7|34.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|34.9|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||30.8|39.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|36.8|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2010 mid-year population estimates||||28.7|46.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|41.0|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.3|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.0|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.6|San Jose, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.7|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.7|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.3|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.5|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.5|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||5.6|9.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.8|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.0|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.2|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.2|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.6|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.8|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||10.2|13.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.8|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|13.3|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.2|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||11.9|16.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.6|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.7|19.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.6|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|20.7|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||18.5|22.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|26.4|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|44.4|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|7.1|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|13.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.0|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|2.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|2.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|2.6|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||1.4|7.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|5.9|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|6.1|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|7.1|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|11.9|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|12.0|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|16.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|17.5|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||11.6|23.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|17.9|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|19.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|22.7|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|23.1|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|25.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||30.8|39.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|25.5|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|25.6|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||19.8|33.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|25.8|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|28.3|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.3|36.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|28.8|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|34.0|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|47.3|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|50.5|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|62.8|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.5|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.5|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.1|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.1|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.9|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.6|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.6|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.8|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|6.2|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.0|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||4.7|9.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.6|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.9|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|8.4|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|10.5|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|13.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||8.2|18.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|11.9|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|1.1|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.0|3.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|4.2|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.2|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.7|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.8|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.4|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|5.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|5.7|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|7.1|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||5.2|9.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.6|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.6|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.7|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||7.3|12.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.7|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.7|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.8|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.2|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.9|15.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.3|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.3|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||8.9|13.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.8|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||11.9|16.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|37.0|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|0.7|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|0.9|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.2|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.6|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.8|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.1|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.2|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.4|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.6|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.9|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||1.9|4.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.0|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||2.9|5.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.4|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.6|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|8.0|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|7.9|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.4|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.5|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.5|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.0|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.3|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|12.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.5|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||10.0|17.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|15.0|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.9|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|18.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|19.1|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|19.5|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||16.7|22.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|23.7|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|25.6|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|27.1|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||22.4|32.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|27.3|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.8|33.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|35.6|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|40.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||35.7|44.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|47.0|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|84.6|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.7|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.4|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.0|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.4|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.5|San Jose, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.8|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.1|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|7.6|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.3|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.4|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.0|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.5|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.9|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.0|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.8|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||11.2|14.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.1|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|15.4|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|16.6|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||13.4|19.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|16.7|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||14.2|19.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|21.8|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||19.5|24.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|23.8|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|39.1|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||30.9|48.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|47.2|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|7.6|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|10.0|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|13.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.2|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.4|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.6|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.5|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|4.6|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|4.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|6.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||3.0|10.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|3.8|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.2|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.4|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|12.7|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|13.7|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|14.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|15.1|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|17.8|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|19.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|19.4|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||13.0|25.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|19.8|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|22.3|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|22.7|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|23.8|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|29.2|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.9|38.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|31.2|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||24.7|39.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|31.7|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|35.3|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||30.9|39.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|39.0|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|51.4|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|53.0|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|59.6|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||45.6|76.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|1.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.3|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.7|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.6|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.1|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.5|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.7|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||3.7|7.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.7|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.0|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.0|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.9|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|10.6|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|18.6|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||13.0|24.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|2.1|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|4.2|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|5.1|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.0|10.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|7.5|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.5|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.7|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.1|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.9|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|4.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.0|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.5|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||5.5|10.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.9|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|8.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||6.0|10.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|8.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.3|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.3|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.7|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.9|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.0|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||9.3|15.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.1|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.6|17.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|14.7|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.9|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||13.5|18.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|23.3|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|0.5|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|0.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|0.8|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.0|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.1|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.6|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.9|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.0|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.0|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.4|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.9|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.3|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||3.0|5.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.4|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||3.0|5.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.7|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||2.8|10.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|8.5|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|7.2|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|7.9|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.0|Los Angeles, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.4|Houston, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.|Adjusted rate of Harris County. Age adjustment uses 2000 standard population. S, indicates numerator too small for rate calculation. Intentional self-Harm (suicide) by discharge of firearms (X72-X74) only.|Includes all of Harris County, not just Houston||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.0|San Diego County, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01.4 as well.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.6|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.2|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||10.6|18.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.2|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|18.5|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|18.5|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Firearm-related deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|18.7|Washington, DC|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|20.4|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.0|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.4|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||18.4|24.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|24.2|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|29.4|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|30.4|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||25.4|36.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|31.5|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.5|38.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|33.3|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||25.8|42.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|40.8|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||36.3|45.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|44.6|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|89.6|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.9|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.7|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||3.0|4.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.1|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.1|San Jose, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.4|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.7|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.8|7.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.0|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||7.0|11.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.2|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.3|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||8.9|11.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.2|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.2|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.5|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||10.9|14.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|13.2|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.6|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||14.6|18.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|17.3|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.0|20.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|19.6|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||15.5|24.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|21.1|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||18.2|24.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|21.6|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|38.7|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|0.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|6.6|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|11.3|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|22.4|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|1.3|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||0.7|2.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|2.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|2.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.3|5.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|3.0|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|4.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|5.2|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|6.0|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.3|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|9.7|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|13.6|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.2|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||10.3|19.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|16.0|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|17.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||11.5|22.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|18.3|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|24.4|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||17.1|33.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|26.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||22.3|29.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|27.9|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|29.6|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|29.8|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|33.1|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.5|42.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|33.7|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|38.8|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||31.7|47.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|38.9|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|42.2|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|46.3|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||33.9|61.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.6|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.6|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.7|3.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.7|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.8|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||3.1|7.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.9|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||2.9|8.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.4|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.6|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.9|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||4.6|9.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.9|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|10.8|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|12.3|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|12.3|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||7.8|16.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|5.8|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|18.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|1.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.0|3.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|6.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.2|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.5|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||1.6|3.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.5|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|4.8|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.|per 100,000 population using annual WA State Office of Financial Management population estimates, age adjusted to the year 2000 standard population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.2|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|6.0|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.4|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.9|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||5.8|9.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.8|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||6.7|11.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.9|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.5|10.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|10.2|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|11.1|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.9|15.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|11.2|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||9.1|13.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.0|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.7|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.4|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.1|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.9|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||13.5|18.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|19.6|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||12.2|30.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|0.4|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|0.9|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||0.5|1.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.8|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.0|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.3|2.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.2|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.4|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||1.4|3.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.0|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.1|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||2.1|4.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.5|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.6|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.9|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||2.9|5.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.9|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||2.3|9.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.0|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.7|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.6|New York City, NY|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|NYC DOHMH Bureau of Vital Statistics|Rates are ge-adjusted to the US 2000 Standard population; ICD10 codes are same as recommended; rate includeas all firearm related deaths occuring in NYC, including non-NYC residents; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.7|San Francisco, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Value is reported for a multi-year period, 2013-2015|||5.3|8.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.8|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||10.0|13.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|15.1|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|15.9|San Antonio, TX|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Firearm Related Mortality rate ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|16.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|16.7|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||12.8|21.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.4|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|18.2|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||15.3|21.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|18.3|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|20.7|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|20.8|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|21.0|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||18.1|23.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|25.3|Chicago, Il|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|32.1|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||26.1|39.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|32.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||28.1|36.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|35.2|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||27.3|44.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|38.8|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||33.1|45.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|39.5|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|71.6|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MiVDRS|Included cases with no ICD10 code and code Y87.1 where manner of death was homicide and cause of death was gunshot wound.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.9|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2016|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.9|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.9|6.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.4|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.6|8.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.5|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.9|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.0|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||8.5|11.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.1|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||8.0|12.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.3|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.7|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|14.3|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||12.6|16.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|14.6|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|16.1|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||14.1|18.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|17.5|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.2|20.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|19.8|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||17.0|22.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|20.7|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|10.2|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|11.5|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|12.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|2.2|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|2.3|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|3.4|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.9|5.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|5.5|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||1.9|13.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.8|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2018|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|9.4|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||6.2|13.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|10.0|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|13.6|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|16.9|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|17.8|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|18.9|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||12.5|27.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|20.3|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||10.5|37.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|20.6|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|21.8|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|26.0|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||22.2|29.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|26.9|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||19.8|34.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|30.9|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|32.6|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.0|41.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|34.1|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|36.3|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||29.3|44.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|36.7|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System|||||31.8|41.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|0.0|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.1|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2019|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.9|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.9|4.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|4.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.6|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.8|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||4.0|8.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.3|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||5.3|10.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.8|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||6.2|9.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.2|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|10.3|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|14.0|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||9.2|18.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|16.5|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System|||||4.3|28.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|4.8|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|7.3|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|18.2|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|0.3|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|1.2|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||0.0|3.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|8.1|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.5|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2021|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|4.7|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.9|7.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.6|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||4.7|8.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.1|8.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.4|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.1|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.6|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||8.2|13.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.8|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.9|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|11.6|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||9.0|14.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.2|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.9|16.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|13.6|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||10.7|17.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.4|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||14.0|18.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|17.8|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|20.1|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System|||||11.3|28.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|0.4|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2022|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.3|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.1|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.8|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.2|6.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.0|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||1.8|4.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.3|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||2.2|4.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.6|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||3.3|5.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.6|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.7|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|9.9|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System|||||6.7|13.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.7|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2023|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|9.9|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.2|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||7.0|14.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.7|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||9.1|12.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.5|Long Beach, CA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|16.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.0|Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||13.0|21.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.3|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||14.5|20.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|18.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder Data Request||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.6|Miami (Miami-Dade County), FL|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|24.4|Phoenix, AZ|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|AZ Death Certifcates||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|24.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||21.4|27.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|30.6|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||26.7|34.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|30.9|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.0|37.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|35.8|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|36.3|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||30.8|42.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|61.6|Detroit, MI|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|MDHHS Violent Death Reporting System|||||53.2|70.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.4|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2024|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|4.0|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.0|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.1|6.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.5|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.7|8.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|9.1|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|10.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||9.0|12.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|11.1|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.5|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||11.9|15.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|18.1|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||14.2|22.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|18.9|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||16.8|21.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|19.7|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.2|23.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|28.6|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|15.2|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|4.6|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||1.6|7.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|6.8|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||2.5|15.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|7.2|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|10.8|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|9.5|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2026|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|11.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.2|18.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|11.9|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||8.5|16.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|14.9|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||9.2|22.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|15.1|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|18.1|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||9.0|34.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|22.4|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||15.9|29.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|26.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|29.5|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||25.5|33.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|34.0|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|35.9|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||27.9|45.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|43.4|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||35.7|52.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|45.7|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||33.3|61.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|57.0|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.3|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2027|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|3.1|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.1|4.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|4.0|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||2.5|6.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|5.9|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|6.2|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|7.1|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||4.9|9.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|8.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|8.4|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||6.8|10.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|14.5|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||8.6|22.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|19.5|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||13.8|25.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|9.5|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|14.2|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.2|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2029|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.8|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|6.7|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||4.8|8.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|7.6|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.2|8.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|9.2|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|12.8|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.4|17.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.3|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||11.0|15.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.3|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.5|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||11.4|15.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.6|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||10.7|16.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|14.4|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||11.4|18.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|0.5|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2030|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.4|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.7|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.7|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.1|2.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.3|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||1.3|3.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.9|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||1.9|4.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.9|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||1.9|4.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.2|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.1|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||3.7|6.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.1|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|CDC Wonder W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|2.6|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|0.8|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|3.6|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|1.5|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|3.6|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|6.5|New York City, NY|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|NYC DOHMH Bureau of Vital Statistics; DOHMH population estimates updated Oct 2031|Firearm related deaths (all intents) defined per ICD-10: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|8.0|Boston, MA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|10.6|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||9.0|12.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|12.0|Seattle, WA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||8.6|16.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|16.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.5|San Antonio, TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Bexar County level data|||15.7|21.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.8|Fort Worth (Tarrant County), TX|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|National Center for Health Statistics|||||15.9|21.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|19.1|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|19.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|22.3|Las Vegas (Clark County), NV|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Nevada Vital Records - Clark County Deaths|||||19.3|25.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|33.3|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||25.8|42.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|34.6|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||27.8|41.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|37.0|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||32.7|41.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|37.9|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||32.4|44.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|53.8|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|American Indian/Alaska Native|14.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Asian/PI|4.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Black|36.4|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Hispanic|10.1|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|White|18.0|U.S. Total, U.S. Total|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Health, United States, 2016, HHS/CDC/NCHS, Table 31 https://www.cdc.gov/nchs/data/hus/2016/031.pdf||U01.4 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|6.1|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.3|7.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|9.9|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|17.2|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||13.9|20.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|17.7|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||13.9|22.1
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|17.9|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||15.9|19.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|22.2|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||19.3|25.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|29.5|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|35.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|2.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|9.5|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|15.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||10.4|23.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|26.6|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|31.3|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|33.1|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.5|42.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|46.5|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||38.8|55.8
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|46.7|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||34.1|62.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|63.5|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|3.1|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.1|4.3
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|10.1|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|13.6|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||7.9|21.7
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|13.7|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|13.1|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|5.0|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|7.3|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.0|8.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|8.5|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||4.1|15.5
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|9.5|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.6|13.2
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|12.8|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|12.8|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||9.9|16.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|14.2|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.3|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||1.3|3.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|3.4|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|3.6|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|7.8|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|10.8|San Diego County, CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||9.2|12.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|16.3|Minneapolis, MN|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|Minnesota Vital Statistics||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|26.5|Denver, CO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|30.3|Columbus, OH|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0.|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||24.6|36.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|32.8|Oakland (Alameda County), CA|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||25.4|41.6
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|34.8|Philadelphia, PA|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|PA Eddie-->Vital Statistics|||||30.7|38.9
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|40.8|Indianapolis (Marion County), IN|Report age-adjusted rate of firearm-related ED visits using ICD-9-CM Codes: E922.0-E922.3, E922.8, E922.9, E955.0-E955.4, E965.0-E965.4, E985.0-E985.4, E970, E979.4. Numerator = Firearm-Related ED visits, Denominator = 2010 census population. Compute rates per 10,000 people, age adjusted using the 2000 US standard population.|MCPHD Death Certificate data|||||35.0|47.4
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|54.0|Kansas City, MO|Report age-adjusted firearm-related mortality rate per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. Use ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0|||||||
Injury/Violence|Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All||Portland (Multnomah County), OR|Report age-adjusted firearm-related mortality rate using  ICD-10 codes: W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0. Compute rates per 100,000, age adjusted to the year 2000 standard population.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.2|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.4|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||2.1|5.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.8|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.8|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.1|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||5.0|7.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.1|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||5.1|7.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.2|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.9|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||5.7|8.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.7|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.4|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.5|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||9.5|14.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.0|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.5|14.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|14.1|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|17.1|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|17.5|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates||||13.6|22.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|19.1|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|19.6|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|0.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|1.7|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|2.1|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.0|4.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|3.2|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||2.1|4.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|8.1|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||3.0|19.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|14.2|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||5.7|31.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|16.1|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|16.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||11.2|22.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|25.6|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|27.4|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|27.5|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||21.4|35.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|28.2|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||21.3|36.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|28.3|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|32.9|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|33.4|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|35.8|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|36.8|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|37.5|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|46.0|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||35.9|58.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|49.8|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|56.0|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates||||42.6|72.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.5|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.8|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.6|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||2.7|7.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.7|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.8|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.3|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||4.8|8.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.6|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.6|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.8|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.2|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.2|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||6.6|11.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|14.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|2.9|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.0|6.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|0.9|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.3|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||0.5|3.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.5|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.0|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.1|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.2|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.6|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.7|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.7|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||1.8|3.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||2.1|5.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.7|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||3.3|6.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|5.4|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||3.4|8.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.1|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.7|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.7|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||1.1|2.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.7|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||1.1|6.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.8|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.7|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.1|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.5|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|7.9|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|3.3|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|4.1|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||2.2|7.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|8.8|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.5|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||7.6|11.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||7.6|11.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.1|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.4|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2010-2012|||8.6|12.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.5|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.8|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.7|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|20.9|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.3|26.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|24.8|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|31.4|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|32.5|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|32.7|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2010 mid-year population estimates||||25.2|41.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|34.7|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.2|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.0|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.2|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder: X85-Y09, Y87.1|||||2.0|4.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.6|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.7|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.3|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.9|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.4|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.4|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.9|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.9|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.8|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||8.8|13.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|13.5|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|13.6|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.8|16.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.5|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|18.3|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|19.8|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|21.9|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|45.2|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|5.9|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||American Indian alone||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|52.7|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|0.8|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|1.3|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|1.6|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.0|3.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|2.0|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.6|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.8|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.7|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|13.1|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|13.6|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|14.6|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|16.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||11.2|22.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|17.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|18.6|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|22.9|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|25.3|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|26.2|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|27.2|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||21.2|35.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|28.8|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|29.0|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|30.1|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.8|38.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|31.8|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|34.9|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|38.9|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|49.9|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|51.6|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|62.6|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.5|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.7|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.0|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||1.7|4.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.9|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.6|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.0|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.0|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.3|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.6|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.8|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|8.2|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|8.4|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|9.0|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|10.6|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|15.1|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|6.4|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|56.7|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.4|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.8|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.8|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.9|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.9|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.2|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.4|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.6|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.5|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.6|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||2.5|5.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.0|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.1|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.3|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.4|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.8|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.2|10.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|26.0|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.1|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.2|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.2|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.6|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.0|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||1.1|2.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.0|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.2|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.3|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.3|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.7|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.7|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.0|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.8|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.2|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|7.1|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|9.7|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|3.2|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|3.9|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.1|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.6|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|7.1|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||5.4|8.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.3|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1, Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.6|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|9.0|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|9.8|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.5|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.7|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.5|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.8|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.9|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|18.1|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||14.4|22.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|23.9|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.8|29.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|24.6|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|28.4|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|31.9|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|36.7|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|37.5|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|84.8|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.1|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.2|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder: X85-Y09, Y87.1|||||2.1|4.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.5|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.3|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||3.8|5.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.9|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.2|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.3|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.4|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.6|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.6|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.7|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.6|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.4|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.2|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.2|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||9.2|13.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.6|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.3|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.6|16.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|15.7|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|19.7|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|20.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|24.9|Cleveland, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|42.3|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates||||33.7|52.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|47.9|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|5.8|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||American Indian alone||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.9|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|3.2|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.8|5.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|4.4|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|7.7|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.5|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.9|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|13.7|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|13.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||8.7|18.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|15.8|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|16.9|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|19.4|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|19.9|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|20.1|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|24.5|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|25.0|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|26.1|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|29.5|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.2|38.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|29.5|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|32.4|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|37.8|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|40.5|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|51.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|52.7|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|60.1|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates||||46.1|77.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|2.0|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.1|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||2.5|5.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.4|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.4|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.5|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.8|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.9|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.9|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.5|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.5|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|8.0|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|8.6|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|10.1|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|18.4|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|2.3|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|0.2|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|3.3|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.0|7.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|0.4|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.4|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.7|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1.8|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.0|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.0|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.1|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.6|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.8|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.6|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||2.3|4.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|4.5|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|6.1|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.6|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||5.0|11.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.4|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.5|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.6|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.7|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.7|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1.8|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.1|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.2|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.5|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.7|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||1.7|3.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.7|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.7|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.9|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.8|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.0|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.5|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.3|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|7.2|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates||||4.0|12.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|12.0|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.2|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released January 2014)|Data includes 2010-2012. Population used for  rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U01-U02 as well.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.2|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.5|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||5.2|8.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.1|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.2|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.3|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.3|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.3|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.4|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.8|Los Angeles, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.8|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|15.8|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|18.8|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||15.1|23.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.6|Washington, DC|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|24.3|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||19.2|30.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|29.1|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|34.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|34.8|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2011 mid-year population estimates||||27.1|44.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|36.1|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|86.9|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.6|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.7|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.2|3.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|2.9|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder: X85-Y09, Y87.1|||||1.8|4.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.6|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||2.9|4.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.1|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.0|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.1|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.2|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||4.2|6.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.2|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.4|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||4.4|6.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.7|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.8|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.2|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|13.3|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.6|16.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|14.5|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||12.2|17.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|15.3|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||13.4|17.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|15.9|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates||||12.2|20.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|20.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|26.7|Cleveland, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|40.5|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|3.5|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||American Indian alone||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|5.3|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||American Indian alone||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|24.0|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|0.4|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|1.1|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||0.5|2.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|1.5|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|7.5|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.1|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.4|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.4|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||7.0|17.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|15.0|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|15.0|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|15.0|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|15.3|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||11.3|20.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|17.1|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||11.4|22.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|17.7|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|18.8|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|25.1|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|26.8|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||22.9|30.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|27.5|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||19.7|37.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|30.6|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|31.8|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||24.4|40.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|36.1|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||29.3|44.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|36.3|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|37.4|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|44.0|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|45.6|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates||||33.4|60.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|50.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|2.3|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.5|3.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.5|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||2.6|7.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.5|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.9|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.7|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.7|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.4|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.8|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.8|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|8.9|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|2.0|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|2.3|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|0.7|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.0|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.0|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.4|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.6|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.7|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||1.0|2.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.8|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|1.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.0|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.3|2.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.5|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.6|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||2.5|5.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.6|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.9|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||2.4|5.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|4.0|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|4.3|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||3.1|5.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|6.4|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|16.1|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|0.6|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.2|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||0.7|1.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.4|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.6|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.9|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.1|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.4|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.8|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.9|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||1.7|4.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.9|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.0|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||2.0|4.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.0|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||1.9|4.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.1|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.4|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|3.8|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.0|4.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|4.5|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.0|San Francisco, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Value is reported for a multi-year period, 2013-2015|||4.7|7.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.9|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.3|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.4|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||5.7|9.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.9|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||6.2|10.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|7.9|San Jose, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|8.2|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|ICD-10 *U01_*U02,X85_Y09,Y87.1,Tables 16-17, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|9.0|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Homicide rate ICD-10 codes: X85-Y09, Y87.1,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|10.6|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.1|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.1|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|16.1|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|22.7|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.8|28.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.4|Chicago, Il|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|25.7|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||21.1|31.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|27.0|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2012 mid-year population estimates||||20.2|35.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|28.6|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||24.9|32.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|34.7|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|73.2|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MiVDRS|Included cases with ICD10 codes V09.2 and V87.7 or no ICD10 code, where Manner of death was homicide.|This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.6|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.0|3.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.8|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.6|4.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.8|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder: X85-Y09, Y87.1|||||2.5|5.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.4|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||3.5|5.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.1|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.1||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.8|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.8|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.9|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||5.7|8.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.2|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.3|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||6.0|8.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.8|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.8|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|13.4|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.7|16.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|14.8|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||12.4|17.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|16.9|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|16.9|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics|Homicide deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1||||13.1|21.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|25.5|Cleveland, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|37.4|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System|||||32.9|41.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|5.8|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.2||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|22.9|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|1.0|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|1.5|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.3||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|2.5|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.3|4.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|3.2|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|4.4|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.9|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||5.2|11.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.4|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|14.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|16.3|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|18.2|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.4||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|18.6|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||9.2|35.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|18.7|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|20.2|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|23.2|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||16.6|29.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|25.6|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||21.8|29.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|30.0|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||22.8|38.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|31.9|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|35.0|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|36.5|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||29.5|44.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|41.5|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System|||||36.3|46.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|45.9|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics|Homicide deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1||||33.3|61.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.5|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.7|3.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|3.5|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|3.7|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|3.8|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|4.5|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.5||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.5|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||3.7|7.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.9|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||3.9|7.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.4|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||5.9|9.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.0|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics|Homicide deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1||||4.7|15.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.2|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|1.9|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||0.0|5.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|12.2|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.1|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.3|3.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.8|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.2|2.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.9|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.1|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.4|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.6||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.5|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.7|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||1.8|3.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||1.9|4.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.3|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder: X85-Y09, Y87.1|||||2.0|5.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.6|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|4.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||2.9|6.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|4.7|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||3.3|6.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|5.0|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|5.9|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.6|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.2|10.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.1|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System|||||7.5|22.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|0.8|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.2|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.1|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.7||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.2|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||1.3|3.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.3|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.3|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.4|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||1.5|3.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||1.7|3.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.4|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||2.2|4.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.0|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.5|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics|Homicide deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1||||4.3|12.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|14.5|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System|||||10.6|18.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|3.9|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.0|4.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|4.2|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.3|7.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.0|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder: X85-Y09, Y87.1|||||3.0|7.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|6.4|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||4.9|8.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|7.3|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.0|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Vital Statistics Report, Final Deaths, 2014, Tables 16-17, ICD-10 U01-U02,X85-Y09,Y87.8||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|9.0|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.9|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||8.8|12.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|10.9|Long Beach, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|12.4|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||10.1|14.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|13.3|Phoenix, AZ|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.0|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.5|Miami (Miami-Dade County), FL|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|22.0|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.1|27.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|24.8|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||20.4|30.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|26.3|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||22.7|29.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|26.6|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County Vital Statistics|Homicide deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1||||19.8|34.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|28.6|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|62.6|Detroit, MI|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MDHHS Violent Death Reporting System|||||54.1|71.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.9|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.3|3.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.3|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.0|5.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.9|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1|||||2.7|5.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|4.3|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|4.5|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|5.3|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||4.2|6.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|5.7|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.3|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||5.2|7.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.5|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||5.3|7.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.6|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|8.4|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|14.1|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||11.7|16.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|14.3|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||11.6|17.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|17.4|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates||||13.6|22.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|18.1|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||16.1|20.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|22.2|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Age-adjusted rates based on small numbers are unreliable and have been suppressed.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|10.3|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.2|16.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|12.2|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|12.6|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||9.1|17.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|14.2|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||8.8|21.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|21.5|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||15.1|27.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|31.4|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||27.2|25.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|32.5|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|33.2|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.5|42.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|37.9|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||30.8|46.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|40.4|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|47.5|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates||||34.9|63.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|58.4|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.2|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.4|3.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|3.4|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||2.1|5.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|4.1|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|4.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|5.8|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||3.8|7.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|6.7|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|7.0|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||5.6|8.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|12.7|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|15.6|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates||||9.4|24.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|20.5|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||14.6|26.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.0|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.1|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.4|3.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1.6|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|2.6|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.8|3.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|2.7|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.4|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||2.3|4.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.5|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||2.0|4.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.7|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||2.3|5.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|4.3|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||3.0|5.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|4.4|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|6.1|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||3.8|9.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.7|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.5|4.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.8|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.1|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||1.3|3.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.2|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.6|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||1.7|3.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.1|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||2.0|4.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||2.0|4.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|4.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.6|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|2.5|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|1.0|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|4.9|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|1.8|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|1.7|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.1|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.2|5.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.8|Seattle, WA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.7|8.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|6.3|Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder X85-Y09, Y87.1|||||4.1|9.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|7.6|Boston, MA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|8.0|New York City, NY|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, ICD10 code is different from what is recommended (NYC does not include Y87.1); age-adjusted rate per 100,000 population.|||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|8.1|Fort Worth (Tarrant County), TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|National Center for Health Statistics|||||6.4|10.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|9.1|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|9.5|Las Vegas (Clark County), NV|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Nevada Vital Records - Clark County Deaths|||||7.6|11.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|10.8|San Antonio, TX|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Bexar County level data|||8.7|12.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|11.2|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|12.6|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|23.3|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||19.1|28.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|23.4|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||18.4|29.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|32.3|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2015 mid-year population estimates||||24.9|41.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|34.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||30.1|38.3
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|40.5|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|American Indian/Alaska Native|9.8|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Asian/PI|2.3|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Black|35.4|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Hispanic|7.9|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|White|3.6|U.S. Total, U.S. Total|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Health, United States, 2016, HHS/CDC/NCHS, Table 29 https://www.cdc.gov/nchs/data/hus/2016/029.pdf||U01-U02 are included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. They are not part of ICD-10 codes.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|3.2|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.6|3.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|7.3|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|7.9|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|13.7|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.9|16.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|15.7|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates||||12.2|20.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|16.9|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||14.3|19.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|17.1|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||15.1|19.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|22.1|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|55.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|17.7|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||11.9|25.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|22.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|27.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|32.6|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||28.4|36.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|33.5|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||25.8|42.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|41.7|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||34.5|50.4
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|46.1|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates||||33.6|61.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|62.4|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|3.2|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.2|4.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|6.0|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|12.6|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||8.3|16.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|13.0|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates||||7.6|20.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|13.1|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|8.3|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|1.7|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||1.1|2.5
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|2.1|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|3.3|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||2.0|4.6
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|3.4|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|4.9|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|1.5|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||0.9|2.2
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.7|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.8|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||1.7|3.9
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|2.9|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|6.1|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|4.9|San Diego County, CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.9|6.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|11.5|Minneapolis, MN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Minnesota Vital Statistics||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|12.9|Denver, CO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|22.0|Columbus, OH|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||17.2|27.7
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|28.5|Oakland (Alameda County), CA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|Alameda County vital statistics files|Using 2016 mid-year population estimates||||21.7|36.8
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|30.2|Indianapolis (Marion County), IN|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|MCPHD Death Certificate data|||||25.3|36.0
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|32.2|Philadelphia, PA|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|PA Eddie-->Vital Statistics|||||28.3|36.1
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|39.4|Kansas City, MO|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|||||||
Injury/Violence|Homicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All||Portland (Multnomah County), OR|Homicide deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X85-Y09, Y87.1|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|2.6|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||2.0|3.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|3.9|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||2.5|6.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|4.3|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.5|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates||||3.5|8.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.6|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|5.8|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.5|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.7|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||5.1|8.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.7|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.4|7.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.4|10.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.9|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||8.9|12.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.3|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.4|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.1|12.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|10.1|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.3|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.6|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|15.7|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|70.5|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|2.1|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||1.3|3.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|3.5|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|4.0|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|4.3|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|8.1|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|9.0|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.7|13.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|11.4|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|5.7|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||5.7|19.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|6.8|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|7.3|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||3.3|11.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|7.7|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|7.7|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.4|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.4|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||6.2|10.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.5|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.6|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||4.5|15.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|9.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|9.8|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|10.0|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|11.4|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|12.7|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|15.0|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.0|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.6|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||1.2|5.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.7|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|5.2|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|5.5|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|5.6|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|6.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.4|9.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.2|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.3|11.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|8.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||8.6|12.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.2|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.7|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.9|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|10.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|4.8|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|5.2|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|10.6|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1.9|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||1.2|3.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.3|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|2.7|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|3.1|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||1.7|6.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.2|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.9|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|6.2|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.4|8.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|6.6|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.3|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.7|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||7.7|13.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.2|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.5|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.8|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.7|14.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|10.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||8.7|12.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.9|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|12.1|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|1.9|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||1.2|2.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.7|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.7|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.8|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.1|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.2|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.3|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||4.3|7.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|5.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|5.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.2|7.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.1|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.5|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.7|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|3.3|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2010-2012|||2.3|4.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|4.3|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|5.9|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|7.6|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|7.8|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|8.2|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.1|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.4|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2010 mid-year population estimates||||5.6|14.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||7.3|11.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||9.6|13.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|12.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||12.6|18.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.6|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.9|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.9|18.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|14.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.6|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.2|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|19.3|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|2.9|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.3|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.4|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.7|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.5|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|4.5|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||3.1|6.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.4|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.7|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.9|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.4|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.7|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|6.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.5|8.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.2|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.3|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||5.6|9.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||6.5|9.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.0|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.8|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.8|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.7|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.3|13.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.9|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.1|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|11.5|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|16.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|3.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.0|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.7|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.8|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|5.4|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|5.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||1.1|10.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|6.1|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|3.4|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|6.3|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|6.4|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|7.0|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|7.2|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.2|9.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.3|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.4|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.6|13.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.8|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|9.0|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|9.8|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|11.3|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|12.1|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|14.1|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|15.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.0|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.4|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.4|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.8|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.1|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.2|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.0|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.0|10.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.1|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.2|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.7|9.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|7.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|9.3|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|9.9|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|10.2|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|13.2|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|1.2|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||0.0|3.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|3.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|8.5|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|74.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1.9|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.6|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.8|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|2.9|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|3.5|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.1|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||2.7|6.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.3|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.5|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|5.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|5.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.2|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.6|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.9|8.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.3|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|9.4|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.5|11.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|10.6|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.3|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.9|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|12.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.0|16.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.8|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1.9|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.1|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||1.7|3.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.0|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.1|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.4|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.7|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.7|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.0|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||2.8|5.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.0|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.5|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.3|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.4|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.6|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.3|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|4.7|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.1|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.3|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.9|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.8|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.9|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.4|10.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.0|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|8.2|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|9.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|9.9|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.7|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.8|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||9.0|14.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|11.6|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||9.4|13.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.2|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.6|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|15.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.1|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.5|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.1|22.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.8|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.1|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|4.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.0|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.2|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.9|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||4.4|7.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.0|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.3|8.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.1|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.1|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.5|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.6|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.7|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.5|8.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|7.3|Cleveland, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|7.8|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.7|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.4|10.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.7|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.9|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.9|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.5|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.0|13.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.7|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.4|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.9|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.9|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||8.3|19.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|5.4|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|16.3|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|26.8|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|39.4|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.3|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|1.4|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.6|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|3.7|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|4.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|5.4|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|9.1|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.5|13.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|3.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|4.2|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|6.4|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.2|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.6|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.6|9.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.2|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.6|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.9|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.1|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||4.4|16.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.3|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.4|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.1|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.3|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|11.8|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|12.2|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.2|17.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|12.8|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|12.9|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.2|19.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|13.4|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|14.4|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|16.0|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.4|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.1|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.3|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.0|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.1|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.3|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.9|9.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|7.7|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|8.0|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|8.7|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|9.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|10.0|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|34.1|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|3.7|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|8.3|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|74.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|2.1|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|3.5|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|4.6|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|4.9|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.3|6.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.2|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.3|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.6|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.7|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.0|7.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|6.5|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|8.7|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.2|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.2|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.4|11.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.3|13.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.9|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.0|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.2|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.1|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.1|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.3|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.0|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.2|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.3|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.4|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.4|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.9|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.1|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.2|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||2.9|5.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.3|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.7|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.9|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.1|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||3.6|6.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.1|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||3.1|7.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.1|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.9|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.4|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.5|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.9|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|7.9|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||5.0|11.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.5|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.9|Los Angeles, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.9|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.8|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|6.9|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.4|10.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|7.2|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.6|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.7|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.9|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.1|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||7.3|11.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.1|Washington, DC|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.3|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.ICD-10 codes V30-V39 (.4-.9), V40-V49 (.4-.9), V50-V59 (.4-.9), V60-V69 (.4-.9), V70-V79 (.4-.9), V81.1, V82.1, V83-V86 (.0-.3), V20-V28 (.3-.9), V29 (.4-.9), V12-V14 (.3-.9), V19 (.4-.6), V02-V04 (.1, .9), V09.2, V80 (.3-.5), V87 (.0-.8), V89.2.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.4|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2011 mid-year population estimates||||6.4|15.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.0|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.3|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||10.1|14.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|13.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.4|18.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.4|Fort Worth (Tarrant County), TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.4|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.9|Houston, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|16.5|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|16.7|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|19.9|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.0|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.6|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.8|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2013-2015|||3.1|4.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|3.9|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|4.7|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.3|6.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.3|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.5|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||4.9|8.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.0|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.8|8.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.1|Cleveland, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.1|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.6|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||5.7|9.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.7|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||5.1|11.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.2|10.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.8|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.0|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.1|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.7|10.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.9|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.6|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||10.0|13.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.9|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||10.3|15.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|6.8|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|15.4|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|2.4|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2013-2015|||1.6|3.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|3.7|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|4.8|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|7.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|8.3|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||3.9|12.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|8.5|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|11.4|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|3.8|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|6.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.8|8.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|7.5|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.4|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|8.7|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|9.7|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|10.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||6.1|15.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.2|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2013-2015|||6.3|18.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.3|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|11.5|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|12.2|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|12.3|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||8.4|17.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|12.5|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|14.4|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||11.4|17.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|15.0|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|1.8|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.1|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2013-2015|||1.5|5.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.0|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.2|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.2|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.7|8.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.4|8.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.8|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|8.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.2|12.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|8.6|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||5.6|12.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|8.7|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.3|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.5|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|9.7|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|16.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|2.1|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|4.8|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|1.1|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||0.0|3.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|4.3|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|2.1|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|3.2|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|4.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.0|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|5.4|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|6.2|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|6.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.7|8.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|6.6|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.3|7.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.4|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.5|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|7.8|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||5.7|10.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|9.9|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.9|13.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|10.0|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|10.4|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||8.4|12.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|11.5|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.0|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.3|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.7|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2013-2015|||1.8|4.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.3|4.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.7|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||2.5|5.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.0|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.3|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||3.4|6.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.9|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||2.9|7.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.1|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.3|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.4|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||2.6|10.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.2|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.8|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||5.2|8.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|7.6|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.9|11.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|19.4|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|3.7|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.0|San Francisco, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Value is reported for a multi-year period, 2013-2015|||3.9|6.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|5.4|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.6|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.7|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|8.8|Chicago, Il|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|8.9|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||5.8|13.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|9.7|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2012 mid-year population estimates||||5.8|15.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|10.6|San Jose, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|10.7|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||7.4|14.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.1|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||8.7|13.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|12.3|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Motor Vehicle Mortality Rate ICD-10 codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|13.0|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|13.1|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|13.3|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||11.0|15.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|13.8|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|15.9|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|16.6|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||13.9|19.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.9|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||15.1|24.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|2.8|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.1|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|3.6|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.3|5.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.3|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.4|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.4|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||4.0|7.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.7|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.1|7.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|5.9|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics|Motor vehicale deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||||3.8|8.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.3|Cleveland, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.7|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.4|8.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.0|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.1|7.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|7.7|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.5|10.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.4|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.8|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||8.4|11.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.0|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.1|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||9.1|12.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.8|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|13.2|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||10.5|15.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|5.5|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|16.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|3.1|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|4.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|5.7|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||2.0|14.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|5.8|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|6.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||2.9|10.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|7.0|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|7.9|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.3|11.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|10.2|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|3.4|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|3.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|3.8|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|6.5|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.7|9.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.2|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||7.8|15.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.8|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.9|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|13.3|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|13.4|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||10.5|16.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|14.7|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||9.3|20.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|3.1|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|3.9|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|6.0|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.4|11.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.7|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.9|9.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.0|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.6|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||6.0|11.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.3|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.6|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|9.6|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|12.1|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||9.9|14.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|12.4|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||8.7|17.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|3.7|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|2.3|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||0.0|5.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|1.8|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|2.8|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.1|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.7|5.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|3.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|4.6|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.1|6.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|4.7|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||3.1|6.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|5.0|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|6.6|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.4|7.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.1|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|7.2|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.2|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||2.5|3.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.2|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.5|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||6.7|10.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|8.9|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||6.6|11.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.2|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||6.3|12.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.2|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||7.4|11.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|10.2|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|11.3|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|1.8|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.1|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.8|4.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.2|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.4|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.8|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.9|4.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.1|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.1|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.5|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||3.2|6.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|5.6|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.1|7.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|5.6|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||4.2|7.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.0|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.6|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||4.9|10.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|3.7|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|4.6|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.1|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|5.3|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.0|9.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|6.2|Long Beach, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|7.8|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||5.2|11.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.2|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County Vital Statistics|Motor vehicale deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||||4.8|13.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|11.0|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||8.6|13.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|11.2|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|12.0|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||9.9|14.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|13.9|Phoenix, AZ|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|AZ Death Certifcates||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.3|19.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.7|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.4|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||12.8|17.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.8|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Motor vehicle crash deaths (age adjusted, per 100,000 population), National Vital Statistics System-Mortality (NVSS-M), CDC/NCHS; Population Estimates, Census||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.9|Miami (Miami-Dade County), FL|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.9|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||13.3|18.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.5|Detroit, MI|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MDHHS Vital Records|||||14.8|24.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|2.9|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.3|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|3.8|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.2|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.6|8.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|7.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.2|8.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|10.3|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||7.4|13.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|10.6|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||9.2|12.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|10.7|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|10.8|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|11.4|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.0|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||10.5|13.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.1|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.5|15.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|16.9|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|2.4|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|4.6|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||1.4|12.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|4.9|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|10.5|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||5.2|15.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|13.9|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||7.2|24.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|3.5|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|5.6|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston Resident Deaths, Massachusetts Department of Public Health |Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|6.1|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|6.4|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.5|8.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|10.7|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||7.4|14.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|12.2|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|12.3|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||6.6|21.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|12.7|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||7.7|19.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|16.6|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||10.9|22.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|18.8|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|18.8|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||12.2|27.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|20.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.7|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|3.7|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston Resident Deaths, Massachusetts Department of Public Health |Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|6.6|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.3|9.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|8.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|8.6|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.8|10.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|10.2|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|10.3|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||7.3|14.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|10.9|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.6|14.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|11.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||9.4|13.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|11.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|3.1|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||0.0|7.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|11.2|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|2.2|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|2.9|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|2.9|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.5|5.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|3.2|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston Resident Deaths, Massachusetts Department of Public Health |Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|4.9|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.4|6.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|5.9|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||4.2|8.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|7.4|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|7.4|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||3.5|13.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|9.0|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||7.1|10.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|9.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|11.4|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||9.2|13.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|11.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||9.0|14.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|11.8|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.5|16.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|11.8|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|1.9|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.1|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.9|4.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|2.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||1.4|3.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.1|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.5|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||2.7|4.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|3.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.3|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||2.8|9.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.6|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||4.3|7.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.8|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||4.3|7.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.2|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.4|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.5|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||5.0|8.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|7.7|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.9|11.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|3.1|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.0|New York City, NY|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.5|Seattle, WA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.4|8.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|4.6|Boston, MA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|10.3|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||8.0|12.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|10.3|Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||7.4|13.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|10.5|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||9.0|12.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|14.9|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|15.2|Fort Worth (Tarrant County), TX|Motor vehicale deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|National Center for Health Statistics|||||12.7|17.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|15.6|Las Vegas (Clark County), NV|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Nevada Vital Records - Clark County Deaths|||||13.0|18.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|15.8|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2015 mid-year population estimates||||10.7|22.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|16.0|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|16.7|U.S. Total, U.S. Total|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|17.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.8|23.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.1|San Antonio, TX|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|CDC Wonder||Bexar County level data|||15.3|20.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|6.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|7.2|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||5.9|8.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|7.3|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.4|8.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|8.6|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||6.1|11.8
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|8.9|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||7.0|11.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|11.5|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|12.8|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.0|16.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|13.5|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|16.7|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|5.5|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.5|8.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|11.4|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|6.5|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||4.6|8.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|11.2|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.6|17.7
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|12.2|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|14.3|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||8.2|23.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|15.1|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.9|22.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|17.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|20.0|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|7.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.5|8.9
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|10.8|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||6.6|15.1
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|18.8|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|35.6|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|4.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|4.5|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|5.6|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||3.8|7.3
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|7.1|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.8|8.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|7.9|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|11.5|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.2|15.6
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|11.7|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|4.1|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||2.7|5.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|4.4|San Diego County, CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.4|5.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|4.6|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|4.6|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||2.2|8.5
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|7.3|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.6|11.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|5.4|Minneapolis, MN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Minnesota Vital Statistics||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|10.9|Philadelphia, PA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|PA Eddie-->Vital Statistics|||||8.5|13.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|13.3|Oakland (Alameda County), CA|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2.|Alameda County vital statistics files|Using 2016 mid-year population estimates||||8.8|19.2
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|15.9|Indianapolis (Marion County), IN|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|MCPHD Death Certificate data|||||12.3|20.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|18.7|Columbus, OH|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.0|24.4
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|19.1|Kansas City, MO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|23.3|Denver, CO|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All||Portland (Multnomah County), OR|Motor vehicle deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, V89.2|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|4.4|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.3|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|6.8|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|7.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|7.8|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.1|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates||||5.6|11.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|8.3|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.4|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.6|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|9.9|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||8.7|11.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|10.5|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.0|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.5|14.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.0|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|11.1|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||8.7|14.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|12.1|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||9.9|14.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|16.2|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|18.9|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||17.0|20.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|3.0|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|3.9|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|5.0|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|5.4|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|5.5|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||1.8|15.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|5.7|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||4.3|7.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|5.8|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|6.2|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||2.7|9.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|3.5|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|3.9|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|4.3|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|5.1|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|5.1|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|5.8|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|6.6|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|6.7|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|6.7|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|6.9|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|8.9|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||4.8|13.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|9.2|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||5.1|15.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|13.3|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||4.7|31.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|15.0|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.4|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|2.9|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|3.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|4.1|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|5.0|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|5.0|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||4.5|9.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.1|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||5.4|9.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.3|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||4.7|11.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|7.6|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|8.1|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|9.9|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|11.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|3.0|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||0.0|7.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|8.9|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|4.7|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|7.5|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|8.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|9.0|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.0|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.2|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates||||6.1|18.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|11.6|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|12.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||9.7|17.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.1|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||11.0|15.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.3|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|13.6|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.0|18.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|14.1|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|15.1|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||12.1|18.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|15.1|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|15.8|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|17.2|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||13.7|20.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|18.9|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|19.1|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|20.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|29.2|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||25.9|32.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.8|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.8|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.9|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|2.9|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|3.9|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.0|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.7|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|4.7|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||3.6|6.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|5.0|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|5.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|5.3|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||3.1|9.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||4.5|7.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.2|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|6.4|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|7.5|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|8.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||6.4|9.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|6.2|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|7.3|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|9.7|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|10.3|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|11.6|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.2|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|13.4|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2010 mid-year population estimates||||8.6|20.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|14.6|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|14.9|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2010-2012|||12.8|17.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.7|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|15.8|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|16.7|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||13.7|19.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.0|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.5|22.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.1|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||12.8|22.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.7|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.9|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|17.9|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|18.4|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||14.4|23.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|25.2|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|30.0|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||26.6|33.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|3.8|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.8|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|5.9|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.8|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|7.9|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.1|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.7|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|8.9|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|9.7|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.1|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.1|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.2|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.3|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|10.8|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.3|13.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.7|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|11.9|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|12.3|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2011, http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_03.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|12.8|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||10.5|15.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.0|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|14.4|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||11.7|17.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|15.6|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|16.5|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||14.7|18.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|0.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|24.0|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|1.1|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|2.5|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|4.4|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|5.6|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|6.7|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|7.4|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|8.3|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics Files, 2011-2013||Oakland; Does not include Pacific Islander||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|9.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||4.8|13.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|16.9|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|3.1|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|4.3|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|4.6|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.3|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.7|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.8|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|5.8|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|6.5|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|7.1|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|7.1|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|7.3|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|8.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||4.0|12.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|10.7|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|36.4|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.5|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.7|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|2.8|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|3.6|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.6|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|4.8|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.1|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.2|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|5.8|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|6.3|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|6.7|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|6.7|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|8.3|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||5.8|10.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|13.1|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|13.2|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|3.7|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|74.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|4.5|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|6.5|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.0|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|8.6|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.3|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|11.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|12.4|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|12.5|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.6|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|13.6|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.0|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.3|18.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.2|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|14.8|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.8|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||12.7|18.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.9|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||12.7|19.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|15.9|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|16.2|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.1|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|17.8|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|18.3|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|18.3|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|23.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||20.5|26.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.0|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.0|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|2.7|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.2|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.5|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.7|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|3.9|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.0|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.2|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.6|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.6|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|4.8|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.0|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|5.5|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.2|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||3.9|9.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|6.6|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|7.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|9.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||7.4|11.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|5.7|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|6.6|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|9.4|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|10.0|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|12.3|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.8|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.8|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|13.8|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|14.3|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics Files, 2011-2013||Oakland||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|15.1|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.3|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.4|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.6|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|16.8|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.0|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.5|22.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|17.1|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|18.7|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|21.3|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||17.0|26.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|22.9|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||18.3|28.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|23.9|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|23.9|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||20.8|26.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|38.8|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|3.8|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.4|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|5.5|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.4|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|6.7|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|7.1|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|7.2|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|8.3|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|9.0|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.1|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.6|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|10.7|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|11.9|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.6|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2013, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|12.8|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.5|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||11.2|16.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.8|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||11.0|17.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|13.9|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.0|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates||||9.3|20.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.5|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|14.6|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|16.4|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||13.5|19.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|18.0|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||16.1|19.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|19.4|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|3.4|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|30.5|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.5|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|2.6|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|5.5|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|6.0|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|6.5|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|6.6|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|10.8|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||5.9|15.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|11.8|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|3.1|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|3.3|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|3.3|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|4.8|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|5.3|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|5.4|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|6.1|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|6.1|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|6.6|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.5|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|7.6|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|8.3|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|9.5|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.1|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|10.6|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|12.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||7.2|17.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|13.4|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|13.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|36.5|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|1.9|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|3.9|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|4.3|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.1|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.3|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.3|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.5|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|5.7|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.0|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.6|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||4.3|8.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|6.6|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|12.5|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|19.3|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|6.7|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||0.0|14.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|7.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|8.0|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|4.8|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|5.5|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|7.6|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|9.2|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.4|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|10.6|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates||||5.7|18.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|11.6|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|11.9|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|12.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|13.3|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.2|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|15.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.2|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.2|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.3|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||13.1|20.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|16.5|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|17.0|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.9|21.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|17.2|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||14.0|20.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|17.4|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|17.9|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|18.9|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|19.0|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|22.0|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|23.9|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|26.3|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||23.2|29.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.2|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.2|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.5|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|2.7|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.1|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.2|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.5|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.5|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|3.9|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|4.4|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|5.1|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.2|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|6.4|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|7.0|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||4.6|10.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|7.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|9.8|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||7.9|11.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|10.3|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.5|Los Angeles, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|5.8|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|8.5|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|9.3|Washington, DC|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|10.3|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|11.9|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2011 mid-year population estimates||||7.7|17.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|12.4|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|14.6|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|15.4|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|15.7|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|16.7|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|16.8|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|17.2|Houston, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 10, 2015.||Harris County data, not just Houston||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|19.6|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files; SANDAG January 1 population estimates (2001-2013 estimate released Jan 2014)|Data includes 2010-2012. Population used for rate is from SANDAG (not ACS). Adjusted rates are adjusted to 2000 U.S. Standard Population.Includes ICD 10 codes U03 as well.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|19.7|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|20.4|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.2|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.1|27.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|21.4|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|22.9|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||18.5|28.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.4|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||21.1|31.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|26.6|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||23.4|29.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|31.4|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|40.0|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|5.5|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|6.3|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.3|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|7.8|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.1|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|8.3|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2013-2015|||7.3|9.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|9.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||8.1|11.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.3|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|10.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.1|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||9.6|12.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.5|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.9|14.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|11.9|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.5|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates||||9.3|16.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.6|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2013, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|12.9|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||11.7|14.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.5|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|16.8|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||14.0|19.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|17.3|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||15.5|19.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|11.7|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||American Indian alone||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|13.3|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||American Indian alone||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|44.9|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|American Indian alone||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|4.0|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|4.6|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|5.0|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2013-2015|||3.7|6.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|5.9|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|7.0|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|7.6|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.2|10.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|8.2|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|10.8|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|15.3|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|4.7|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.1|6.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|4.8|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|4.9|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|5.6|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|5.6|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|5.8|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|6.4|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|6.6|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|6.6|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|7.6|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2013-2015|||4.0|13.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|7.9|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|9.5|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates||||4.6|17.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|10.2|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|16.9|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|3.3|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.3|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||2.7|6.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.6|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.6|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|4.6|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.1|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.7|6.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.5|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|5.7|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.0|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.7|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.2|10.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|6.8|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|7.3|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||5.0|9.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|8.5|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2013-2015|||5.7|12.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|10.1|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|15.8|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|12.9|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|11.1|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|8.4|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|9.0|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|10.7|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2013-2015|||8.9|12.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.3|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|12.9|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.2|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.4|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|13.8|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||11.1|16.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|14.1|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||10.4|18.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|15.5|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|16.1|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|16.2|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||13.7|18.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|16.5|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|17.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|17.5|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||14.4|20.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|17.8|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||15.8|19.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.3|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|18.4|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||15.0|22.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|21.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|23.9|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||21.0|26.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|25.8|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates||||17.5|36.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|1.9|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.3|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.3|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|2.5|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.1|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|3.4|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.2|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.2|6.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.6|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.8|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||3.5|6.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|4.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.0|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.5|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, 2013, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|5.7|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.6|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|6.6|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.4|7.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|7.8|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||5.3|11.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.0|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates||||4.7|12.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|8.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||6.4|9.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|9.8|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|6.6|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|8.8|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|10.0|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated July 2013; ICD10 code is same as recommended; age-adjusted rate per 100,000 population.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|10.1|San Jose, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Santa Clara County Public Health Department, 2011-2013 Death Database, U.S. Census Bureau, Census 2000-2010||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|11.8|Chicago, Il|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|12.3|San Francisco, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Value is reported for a multi-year period, 2013-2015|||10.5|14.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|SA Metro Health Death Certificates supplied by Texas DSHS, Preliminary data subject to change.|Suicide rate ICD-10 codes: X60-X84, Y87.0,  NH= Non Hispanic|Bexar County (Not just San Antonio)||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.2|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|14.6|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MiVDRS||This is the rate based on city of residence, not city of injury.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|15.4|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||12.6|18.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.0|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.1|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|WA State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2013, August 2014.||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.3|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|17.7|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2012 mid-year population estimates||||12.4|24.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|18.8|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.0|24.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.0|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.4|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|19.7|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||17.5|21.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|20.3|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, 2013, http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.4|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Colorado Vital Records Death Data|2011-2013 years are the most recently available data at this time.|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|24.8|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||20.3|30.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.5|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||21.3|31.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|26.9|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||23.7|30.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|4.7|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|6.4|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.0|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System|||||5.9|10.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.1|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|8.6|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|9.1|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.0|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||8.4|11.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.3|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||8.8|11.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.4|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||8.1|13.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|10.6|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics|Suicides per 100,000 population using using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0||||7.8|14.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|11.3|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||9.8|12.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|12.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|12.5|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||11.3|13.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|13.0|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.0||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|13.4|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|13.8|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||11.4|16.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|14.2|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||11.3|17.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|15.6|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|16.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|17.9|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||14.9|20.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|18.5|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||16.6|20.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|10.9|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.1||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|13.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|6.0|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.2||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|6.4|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|6.7|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|6.8|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.5|9.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|7.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||3.5|11.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|9.7|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||4.6|20.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|9.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.8|19.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|13.7|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|18.2|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|3.4|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|3.7|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|4.0|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|4.3|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|5.2|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|5.5|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.8|7.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|5.7|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.3||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|6.1|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System|||||4.1|8.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|6.6|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|7.4|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|11.1|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|13.7|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||8.5|18.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|2.5|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|4.0|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.1|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||3.4|7.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|5.3|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|6.1|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|6.3|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.7|8.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|6.3|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.4||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.3|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||5.7|9.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|7.9|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|8.1|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||4.0|12.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|11.0|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|12.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||8.7|15.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Population denominators based on extrapolation||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|9.4|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|0.0|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|4.6|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|5.5|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||0.1|10.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|5.6|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|9.3|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|11.2|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||8.4|15.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|11.3|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||8.4|15.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|12.9|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||10.3|15.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|13.9|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|13.9|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|14.9|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.1|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.4|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|15.9|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||13.4|18.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.2|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System|||||8.5|23.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|16.4|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.5||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|17.3|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||13.8|20.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|17.6|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||13.5|22.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|17.7|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||15.7|19.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|18.2|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||14.8|22.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|19.6|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||16.2|23.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|20.3|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|21.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|24.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||21.5|27.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|24.4|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics|Suicides per 100,000 population using using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0||||16.2|35.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|2.8|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.4|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.4|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.6|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|3.7|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||2.4|5.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|4.6|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||3.3|6.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|5.7|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.6|7.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|6.9|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|7.9|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||5.5|11.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|8.2|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||6.5|10.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|8.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|8.4|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||5.8|11.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|8.9|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||6.0|13.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|9.6|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|8.4|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|9.5|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|12.3|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||8.8|17.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|12.7|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||9.0|17.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|13.0|Detroit, MI|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|MDHHS Violent Death Reporting System|||||9.2|16.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.2|Long Beach, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|14.6|Miami (Miami-Dade County), FL|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.1|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||12.5|17.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|15.2|Cleveland, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Cuyahoga County Medical Examiner's Annual Report||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|17.4|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||14.4|20.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|18.2|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County Vital Statistics|Suicides per 100,000 population using using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0||||12.8|25.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|18.9|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||16.0|21.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.7|Phoenix, AZ|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|AZ Death Certifcates||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|19.8|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||17.6|22.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|20.7|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Vital Statistics Report, Deaths Final Data, 2014, ICD-10 Codes: X60-X84, X87.7||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|21.3|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||16.3|27.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|21.6|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||17.4|26.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|26.6|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|28.2|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder: X60-84 (X87.0 does not appear to exist)|||||22.9|33.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|29.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||25.9|32.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.0|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|6.2|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|11.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||9.4|12.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|11.8|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||10.2|13.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.0|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||10.9|13.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|12.0|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||9.6|15.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|13.6|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||11.3|16.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|14.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|14.7|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||11.8|18.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|15.0|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0|||||12.4|17.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|15.6|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|16.5|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||14.7|18.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|19.0|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|32.7|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|5.9|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|6.4|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.3|9.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|9.5|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||5.2|13.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|9.6|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|11.8|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||6.1|21.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|12.4|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||6.4|23.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|26.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|4.8|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|5.6|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|5.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.9|7.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|6.7|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|8.8|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|8.9|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||4.8|13.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|15.6|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|2.8|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|3.8|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||2.2|5.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|4.0|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|4.8|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||3.5|6.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|7.3|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||5.0|9.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|7.5|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||6.0|9.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|7.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.6|11.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|10.0|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|11.2|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates||||5.4|20.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|4.1|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||0.0|9.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|35.5|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|7.3|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|8.9|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|12.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||10.1|15.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.1|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||10.0|17.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|13.7|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||10.5|18.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|16.1|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0|||||13.1|19.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|16.9|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|17.5|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||14.1|21.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|17.6|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||15.6|19.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|18.0|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||14.5|21.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|18.2|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||14.1|23.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|19.1|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||16.3|21.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|19.3|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|20.4|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||17.7|23.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|20.9|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates||||13.1|31.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|22.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|4.0|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|4.4|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.0|5.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.2|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates||||2.6|9.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.2|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||3.8|6.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.7|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||4.3|7.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|5.9|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.8|7.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.0|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.1|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||3.8|9.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|6.4|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||4.1|9.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|7.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|7.8|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0|||||5.4|10.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|8.5|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||6.7|10.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|9.5|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|American Indian/Alaska Native|6.5|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Asian/PI|4.0|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Black|2.0|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|Hispanic|2.6|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Female|White|7.8|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|7.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|8.7|New York City, NY|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|NYC DOHMH Bureau of Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|9.1|Boston, MA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|16.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||13.6|19.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|17.2|San Antonio, TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Bexar County level data|||14.5|19.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.1|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||13.8|23.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.2|Seattle, WA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||13.8|23.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.6|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||16.5|20.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|18.7|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2015 mid-year population estimates||||13.1|25.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|19.0|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|19.2|Fort Worth (Tarrant County), TX|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|National Center for Health Statistics|||||16.2|22.1
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|21.1|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|22.7|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder X60-84, Y87.0|||||18.2|28.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|24.7|Las Vegas (Clark County), NV|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Nevada Vital Records - Clark County Deaths|||||21.6|27.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|25.1|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|25.3|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||19.8|31.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|American Indian/Alaska Native|18.8|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Asian/PI|9.1|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Black|9.6|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|Hispanic|9.9|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2015|Male|White|26.6|U.S. Total, U.S. Total|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Health, United States, 2016, HHS/CDC/NCHS, Table 30 https://www.cdc.gov/nchs/data/hus/2016/030.pdf||U03 is included in the ICD identifying codes as of 2001 for classifying and coding deaths due to acts of terrorism. It is not part of ICD-10 codes.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|9.1|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||7.6|10.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|10.0|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates||||7.3|13.5
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|10.8|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||8.3|13.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|12.0|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||10.8|13.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|14.3|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder|||||11.7|16.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|18.9|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|19.0|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|31.2|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|7.9|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||5.5|11.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|19.2|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|5.2|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|5.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.9|7.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|10.1|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||6.1|15.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|15.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|15.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|0.0|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|5.3|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||4.0|6.9
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|6.0|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||2.3|9.7
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|8.1|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|10.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|34.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to less than 10 deaths.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 20 observations.|||0.0|0.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other||San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates are suppressed here because they're statistically unstable.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|9.9|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|12.3|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||9.8|14.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|12.8|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||9.4|17.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|15.3|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder|||||12.4|18.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|17.0|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||15.0|19.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|17.3|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||13.9|21.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|18.7|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|22.7|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|24.1|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates||||16.0|34.8
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|4.3|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|4.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||3.2|6.0
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|6.3|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder|||||4.2|9.2
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|9.8|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|11.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All||Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|11.3|Kansas City, MO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|14.6|Philadelphia, PA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|PA Eddie|||||11.8|17.3
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|15.4|Minneapolis, MN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Minnesota Vital Statistics||||||
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|16.0|Oakland (Alameda County), CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Alameda County vital statistics files|Using 2016 mid-year population estimates||||10.9|22.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|17.2|Columbus, OH|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Ohio Department of Health, Office of Vital Statistics.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).|||12.9|22.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|18.3|San Diego County, CA|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC WONDER Online Database, released December, 2017. Data are from the Multiple Cause of Death Files, 1999-2016, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html  Accessed 01/2018.|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||16.3|20.4
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|22.4|Indianapolis (Marion County), IN|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, X87.0|MCPHD Death Certificate data|||||18.1|27.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|22.5|Portland (Multnomah County), OR|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|CDC Wonder|||||18.1|27.6
Injury/Violence|Suicide Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|28.2|Denver, CO|Suicides per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population. ICD-10 Codes: X60-X84, Y87.0|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|583.3|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||574.3|592.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|606.0|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||586.2|626.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|630.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||621.1|639.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|673.5|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||647.4|699.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|686.1|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|751.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||736.9|765.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|755.0|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|777.0|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||763.7|790.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|778.1|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|803.7|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||784.5|823.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|823.3|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|883.6|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||868.9|898.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|918.6|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||892.4|944.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|All|1027.7|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|771.4|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||615.5|927.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native|1476.2|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|346.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||272.6|433.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|390.4|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|407.8|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group. |||367.0|453.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|424.6|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|444.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||400.3|487.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|445.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||421.0|470.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|446.3|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||434.1|458.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|469.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||432.9|505.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|477.6|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||420.8|534.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|553.7|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI|570.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||405.6|779.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|770.2|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|798.8|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|814.7|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||758.3|871.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|847.9|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||788.1|907.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|886.2|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||838.1|934.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|906.3|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||862.1|952.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|907.6|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||816.8|1006.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|958.0|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||904.6|1011.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|978.3|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|979.0|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Missouri Department of Health and Senior Services and Kansas City Missouri Health Department||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|996.1|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||939.1|1053.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|1005.3|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|1028.3|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||1003.2|1053.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Black|1041.3|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||994.9|1089.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|353.8|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||256.1|476.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|418.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|420.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|429.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|500.5|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||422.7|578.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|508.2|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||478.4|538.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|515.1|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||393.4|666.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|522.3|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||501.4|543.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|542.5|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||515.2|571.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|614.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||565.0|664.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|718.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||697.6|739.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic|761.1|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Hispanic||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Multiracial|771.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|154.9|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||111.9|197.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|198.6|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|284.8|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||189.2|411.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|333.6|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||212.0|514.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|471.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other|485.1|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||292.0|757.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|Other||Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|609.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||586.0|633.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|616.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||571.0|661.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|628.9|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||614.6|643.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|669.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||658.5|681.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|727.8|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|741.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|760.2|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|770.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|772.5|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||751.5|793.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|795.4|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||773.3|818.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|864.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||844.2|884.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|868.6|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||851.5|885.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|940.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||908.3|972.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Both|White|1282.4|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|470.3|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||459.7|481.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|514.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||490.6|539.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|542.6|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||531.6|553.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|551.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||520.5|582.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|568.4|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|617.1|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|634.6|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||617.7|651.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|659.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|663.9|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||647.1|680.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|683.9|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||661.0|707.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|711.9|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|716.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||699.5|733.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|783.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||751.8|815.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Female|All|995.0|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|711.7|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||Value is reported for a multi-year period, 2010-2012|||696.7|727.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|723.6|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||689.9|758.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|737.6|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||722.8|752.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|833.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2010 mid-year population estimates||||788.0|878.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|836.6|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|899.9|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||875.8|924.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|901.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||880.2|922.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|923.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|934.4|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|943.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|961.7|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||928.5|995.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|1063.1|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|1102.0|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1056.1|1148.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2010|Male|All|1110.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||1084.3|1136.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|355.5|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|610.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|616.9|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|621.8|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|628.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||619.9|637.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|668.0|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|706.8|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|715.8|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|738.3|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||724.6|752.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|752.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||731.6|772.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|753.5|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|779.2|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|783.4|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|797.6|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||778.5|817.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|805.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||791.5|818.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|823.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|911.7|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|938.6|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||912.1|965.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|All|1087.9|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|457.5|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|534.3|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes||American Indian alone|||345.8|788.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|871.9|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||706.9|1036.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|1062.7|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|1065.9|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native|1376.2|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|285.5|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|380.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|412.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|418.3|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|433.5|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||410.2|456.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|435.3|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||373.3|497.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|439.2|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|474.4|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|481.9|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|487.9|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|526.8|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||433.5|620.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|527.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||487.6|566.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI|611.0|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||450.5|810.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|740.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|754.8|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|773.4|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|813.3|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||758.5|868.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|814.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|843.1|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|845.8|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||746.2|945.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|873.0|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|886.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||826.8|946.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|935.3|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|944.2|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|949.8|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||904.9|996.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|954.6|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|961.0|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||910.6|1011.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|986.5|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|990.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||936.4|1044.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|1021.6|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|1045.9|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Black|1055.4|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|368.7|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|387.1|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||274.0|531.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|402.2|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||304.4|499.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|413.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|418.1|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||314.4|549.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|431.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|463.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|485.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|491.9|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|514.4|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|519.6|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||489.5|549.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|520.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|526.9|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||506.6|547.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|531.5|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|585.6|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|688.7|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|712.3|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||692.0|732.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Hispanic|764.8|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|231.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Multiracial|406.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|139.4|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|181.9|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||133.8|230.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|321.1|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Other includes Unknown; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||205.7|477.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|461.9|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston); Other includes Asian/Pacific Islander| Native American| Multiracial||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other|540.7|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|Other||San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|224.7|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|624.8|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|650.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|662.5|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|670.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|670.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||659.6|682.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|722.2|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|737.4|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|739.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|743.9|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||723.3|764.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|748.2|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|752.3|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|769.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||746.9|791.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|771.8|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||749.9|794.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|793.4|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|874.1|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|891.6|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||874.3|908.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|954.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||922.1|986.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Both|White|1382.4|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|299.7|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|511.6|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|520.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|522.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|537.0|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||526.2|547.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|546.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|582.3|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|624.8|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|625.5|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|629.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||612.9|645.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|640.8|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|643.0|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||618.0|667.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|667.8|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||645.1|691.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|673.7|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|686.1|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|697.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||679.9|714.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|748.6|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|805.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||773.8|837.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Female|All|1078.8|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|421.9|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|740.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|741.6|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||726.9|756.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|747.7|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|755.9|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|824.0|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|836.6|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|859.9|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|871.9|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||848.7|895.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|894.2|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||859.1|929.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|901.7|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|912.8|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|926.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||904.8|947.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|974.4|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|978.5|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||945.1|1012.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|980.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|1097.3|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|1115.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1069.3|1161.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2011|Male|All|1134.9|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|341.3|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|507.6|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|573.1|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|600.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|607.3|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|616.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||607.4|624.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|629.0|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|682.9|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|690.3|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|714.5|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|732.8|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|742.8|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||722.6|763.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|744.9|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|756.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|787.5|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|805.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||785.9|824.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|815.5|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||801.8|829.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|820.1|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|823.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Missouri Department of Health and Senior Services and Kansas City Missouri Health Department||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|879.2|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|949.8|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||923.1|976.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|1032.5|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|1049.8|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|All|1704.6|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||1640.3|1769.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|274.0|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|366.2|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|595.3|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|620.9|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes||American Indian alone|||412.6|897.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|628.6|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|875.9|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||710.2|1041.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|1000.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native|1650.2|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|223.9|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|269.8|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|Does not include Pacific Islander||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|358.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|370.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|382.8|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|407.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|408.9|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||387.1|430.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|425.7|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|446.8|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|471.7|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||427.3|516.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|474.6|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|505.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|510.9|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||444.6|577.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|561.2|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||520.6|601.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI|619.2|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||456.5|820.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|566.3|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|716.2|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|720.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|758.0|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||706.4|809.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|771.1|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|774.4|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|802.2|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|839.7|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|853.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|887.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|898.5|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|918.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|938.5|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||894.5|984.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|975.0|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||921.4|1028.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|977.1|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|981.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||875.7|1087.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|982.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|987.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1010.6|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||952.6|1068.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1010.9|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1020.6|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||968.4|1072.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1021.5|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1023.7|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Black|1047.3|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|195.9|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|339.5|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|379.4|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||283.8|500.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|395.8|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|444.0|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||340.6|547.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|474.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||355.7|621.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|480.3|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|505.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|510.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|517.4|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||497.8|537.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|523.4|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|532.5|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|539.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 2. Number of deaths| death rates| and age-adjusted death rates| by Hispanic origin| race for non-Hispanic population| and sex: United States| 19770-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|539.6|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|548.9|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||469.0|628.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|565.0|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||532.9|597.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|567.2|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|567.8|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|663.9|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|681.5|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|685.0|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|697.6|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Hispanic|714.8|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|109.4|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Multiracial|198.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|193.8|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||147.1|240.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|196.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Other includes Unknown; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||120.0|303.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|273.5|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|422.7|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||310.6|562.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|468.1|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston); Other includes Asian/Pacific Islander| Native American| Multiracial||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|Other|616.6|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|224.7|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|587.9|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|625.3|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|630.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|659.0|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|661.2|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||615.0|707.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|662.5|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||651.4|673.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|664.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|710.8|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|712.9|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|724.5|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|727.1|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|745.8|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|746.2|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||724.5|767.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|750.0|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|752.0|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|787.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||765.0|809.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|792.2|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|816.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|831.0|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|883.2|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||866.0|900.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|903.9|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|971.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||938.9|1004.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Both|White|1334.4|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|281.6|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|487.5|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|500.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|500.0|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|503.8|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|515.5|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|521.7|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||511.2|532.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|559.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|578.6|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||547.4|609.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|586.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|601.9|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|624.7|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|629.8|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||605.5|654.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|641.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|642.2|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|646.5|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|654.5|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||632.3|677.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|654.6|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|700.3|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||683.0|717.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|707.4|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|713.8|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|815.7|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||783.5|847.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|829.6|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Female|All|1007.6|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|411.6|Los Angeles, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Linked California DPH Death Statistical Master File for Los Angeles County residents, 2010-2012|Age-specific rates calculated using annual Los Angeles County population estimate created by LAC Internal Services Division, adjusted to year 2000 standard population. Includes records for which Los Angeles was recorded on the death certificate as the decedents city of residence.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|506.6|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|670.5|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|729.6|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||715.3|744.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|730.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|747.7|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|773.2|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|822.4|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|835.5|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|843.7|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2011 mid-year population estimates||||798.7|888.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|853.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|865.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|872.1|Houston, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services, Center for Health Statistics, 2015.||Harris County (Not just Houston)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|883.4|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||848.9|917.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|907.1|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|945.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||923.5|966.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|970.5|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|975.7|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1006.3|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||972.7|1040.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1024.6|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1095.7|Cleveland, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1107.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1127.6|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1081.4|1173.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2012|Male|All|1291.7|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|519.8|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|578.0|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||569.2|587.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|580.7|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|595.9|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|600.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|603.2|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|618.2|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||609.7|626.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|683.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||657.6|709.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|691.0|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|731.9|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|744.2|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||730.4|758.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|749.6|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|759.9|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||739.7|780.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|761.7|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|787.5|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||768.8|806.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|789.1|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|809.9|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|821.3|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|829.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||815.4|842.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|863.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||849.2|877.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|952.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||926.2|979.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|All|1025.5|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|237.2|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|427.9|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|591.7|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|803.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes||American Indian alone|||565.8|1107.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|851.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||689.5|1012.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|1086.8|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native|1641.2|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|243.9|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|300.1|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|334.3|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|360.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|373.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|405.4|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|411.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||389.9|432.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|420.1|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|432.8|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||372.2|493.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|433.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|450.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||390.0|511.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|458.3|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|469.7|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||457.3|482.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|477.2|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||433.2|521.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|507.8|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|611.9|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||568.9|654.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|662.3|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||501.6|858.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI|776.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|559.7|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|710.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|716.4|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|743.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|771.3|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||720.3|822.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|782.8|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|802.7|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|805.1|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|826.8|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||781.3|872.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|885.2|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|933.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||889.7|978.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|936.2|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|949.3|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|955.1|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|979.7|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||935.0|1026.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|982.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||875.7|1088.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|993.7|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|996.2|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|996.5|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||938.9|1054.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|1011.7|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||960.1|1063.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|1034.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||979.3|1090.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Black|1040.8|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|206.4|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|356.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|412.6|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||316.0|533.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|426.3|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||317.4|560.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|432.1|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|478.7|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|481.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||376.2|587.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|503.6|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|510.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|515.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||495.9|534.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|524.3|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||488.0|560.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|535.4|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|544.9|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|553.8|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||476.2|631.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|567.6|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|567.9|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|614.3|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||580.6|648.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|617.1|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||588.1|647.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|657.2|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|665.2|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|686.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Hispanic|792.3|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|143.5|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|308.8|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|423.9|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Multiracial|618.0|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|230.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||176.6|283.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|251.8|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Other includes Unknown; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||165.9|366.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|310.3|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|442.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||322.4|591.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other|444.4|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|Other||Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|580.9|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||567.1|595.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|601.7|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||558.2|645.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|601.9|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|616.8|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|620.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|649.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|649.4|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|667.5|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||656.5|678.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|719.1|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|728.6|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|741.2|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|747.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|747.6|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|755.0|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|767.5|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||745.8|789.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|768.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||746.9|790.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|773.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|774.5|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|782.2|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||765.6|798.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|881.3|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||864.2|898.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|896.3|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Both|White|957.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||925.0|989.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|458.2|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||447.7|468.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|477.4|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|486.0|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|491.7|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|500.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|508.9|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|519.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||509.4|530.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|559.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|571.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||539.9|602.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|579.0|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|607.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|623.5|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|632.6|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|641.3|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||616.9|665.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|654.7|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||637.8|671.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|655.6|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||633.4|678.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|659.3|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|671.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|710.5|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||693.1|727.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|720.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||703.7|737.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|812.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||780.3|844.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Female|All|821.0|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|526.5|Phoenix, AZ|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|707.3|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|719.7|San Francisco, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Value is reported for a multi-year period, 2013-2015|||704.6|735.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|720.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics|Denominator is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates, updated 2014. The death events reported are based on certificates filed with the New York City Department of Health and Mental Hygiene (DOHMH) for vital events occurring in or in-route to New York City, regardless of individual residency status, in a particular year. Any events registered after file closure (typically occurring within 5 months of year-end) are excluded from this report. Such late registrations are rare. Data are age-adjusted to the US 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|737.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||723.5|752.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|738.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|743.3|Miami (Miami-Dade County), FL|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|The mortality rates were calculated using population from Florida Charts between 2010 and 2014.||Miami-Dade County (not just City of Miami)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|789.1|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|828.3|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2012 mid-year population estimates||||784.2|872.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|832.1|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|857.2|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||833.9|880.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|863.6|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2013 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2013. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|914.5|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||879.6|949.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|930.1|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|934.8|Chicago, Il|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|958.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||925.7|991.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|962.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||940.4|983.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|978.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|1010.1|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|1059.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||1034.2|1084.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|1138.3|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1091.8|1184.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2013|Male|All|1292.4|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Michigan Department of Health and Human Services||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|562.1|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|568.7|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||550.3|587.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|580.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|584.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||575.9|592.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|652.6|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|657.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||632.2|682.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|717.6|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||698.1|737.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|724.6|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|735.2|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||722.2|748.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|745.0|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||731.4|758.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|768.7|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|774.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|801.6|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||782.8|820.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|843.1|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||829.2|856.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|857.2|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|861.9|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||847.6|876.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|971.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||945.0|998.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|All|976.3|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|594.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|699.6|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||560.7|838.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|737.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes||American Indian alone|||507.4|1035.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|811.2|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|1050.9|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||American Indian alone||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native|1412.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|360.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|365.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|371.5|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||304.2|438.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|373.1|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|379.1|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||334.4|423.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|388.3|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|407.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||387.4|428.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|412.9|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||357.0|468.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|425.3|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||384.5|466.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|441.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||401.5|484.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|446.1|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|448.2|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||389.9|506.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|562.5|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|650.8|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||606.6|695.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|750.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||570.2|970.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI|751.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|680.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|701.0|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|742.2|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||667.0|824.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|766.0|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|786.0|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||735.6|836.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|853.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||797.7|908.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|870.7|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|877.2|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||831.2|923.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|887.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||789.1|986.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|944.7|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||921.4|968.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|957.1|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||913.7|1002.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|974.8|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|992.7|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||934.4|1050.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|997.8|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|1001.9|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|1017.6|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||965.8|1069.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|1031.8|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Black|1045.3|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||989.3|1101.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|316.5|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||239.6|414.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|385.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|421.3|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|444.5|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|457.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||356.4|581.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|490.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|494.4|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||476.1|512.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|507.2|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||382.1|660.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|523.3|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|526.4|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||491.2|561.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|528.3|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||423.8|632.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|543.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||466.4|619.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|641.4|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||607.5|675.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|648.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|704.7|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||685.8|723.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|719.9|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||669.7|770.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Hispanic|792.1|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|324.9|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990_2014, August 2015.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|397.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||273.6|558.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|417.7|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||319.1|516.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Multiracial|468.7|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|225.0|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||174.4|275.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|264.1|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||153.8|422.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|391.1|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|434.9|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|1062.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||755.8|1486.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|1142.9|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||590.6|1996.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other|3594.6|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|Other||San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|573.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||551.7|596.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|577.0|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|583.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||540.6|626.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|610.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|623.7|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||613.1|634.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|722.7|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|728.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||707.0|749.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|734.1|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|739.7|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|742.8|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|749.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||729.3|769.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|757.7|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||739.3|776.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|762.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|778.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||756.2|800.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|781.5|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||765.0|798.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|803.7|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|885.8|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||868.6|902.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Both|White|979.9|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||947.2|1012.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|465.5|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||443.6|488.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|487.2|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|489.2|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||479.2|499.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|490.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|528.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|547.5|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||517.2|577.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|609.8|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||586.1|633.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|615.5|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||599.8|631.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|616.7|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|620.0|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|638.5|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|645.0|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||628.5|661.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|681.9|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||659.4|705.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|699.3|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||682.0|716.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|701.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|710.3|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||693.7|727.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|801.8|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Female|All|805.2|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||773.2|837.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|654.4|Long Beach, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Source: California Electronic Death Registration System (CA-EDRS), as of April 1, 2016.  Includes immediate cause of death and contributing causes of death.   Includes records for which Long Beach was recorded on the death certificate as the decedents city of residence.  Duplicate records due to revisions to the death certificate are not included.  ICD-10 codes were not included in the dataset; algorithms were written to match key words from cause of death to the corresponding ICD-10 diagnosis.||Deaths for which cause was listed as deferred for review by a coroner or for which cause was missing are not included.  Counts from which rates are derived are subject to change due to late reporting or revisions to the cause of death.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|699.7|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||686.0|713.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|700.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|706.5|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||674.6|739.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|794.6|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County Vital Statistics|All deaths per 100,000 population using 2014 Alameda County population (ESRI modified), age adjusted to the year 2000 standard population||||751.8|837.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|815.6|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|855.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Detailed Tables for the National Vital Statistics Report (NVSR) Deaths: Final Data for 2014 Table 1. Number of deaths| death rates| and age-adjusted death rates| by race and sex: United States| 1940| 1950| 1970| and 1980-2014.||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|856.0|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder: All ICD-10 codes|||||822.6|889.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|869.7|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||846.6|892.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|884.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||862.2|906.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|953.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|955.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics|Rates per 100,000 calculated using the Minneapolis 2010 Census population; age-adjusted using the 2000 US standard population|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|959.7|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||927.5|992.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|1008.5|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||986.3|1030.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|1047.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|1068.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||1043.4|1093.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|1197.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1149.7|1245.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2014|Male|All|1210.4|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Vital Statistics|per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population.|||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|539.0|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||521.4|557.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|580.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|592.3|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||584.1|600.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|688.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||662.9|713.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|690.8|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|730.1|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||716.9|743.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|733.1|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|742.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||729.1|754.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|748.8|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||729.0|768.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|784.6|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|793.7|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|796.6|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||778.0|815.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|849.9|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||836.0|863.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|866.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||852.1|880.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|875.0|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|1000.8|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|All|1046.4|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1018.4|1074.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|596.9|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|649.2|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||441.1|921.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|826.2|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||677.8|974.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native|1593.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|344.7|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||302.8|386.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|360.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|360.3|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||298.3|422.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|394.8|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|395.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||375.7|414.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|403.1|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||366.2|443.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|413.9|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|415.6|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||361.5|469.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|449.5|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||392.6|506.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|477.9|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||436.0|519.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|514.5|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|608.4|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||565.4|651.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|671.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI|923.3|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||723.8|1161.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|690.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|721.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||649.3|800.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|756.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||707.9|804.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|771.7|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|851.9|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|853.4|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||798.7|908.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|886.0|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||840.7|931.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|931.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||908.6|954.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|951.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||908.2|995.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|975.7|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||924.8|1026.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|979.5|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||875.9|1083.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1027.3|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||968.1|1086.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1031.5|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1059.0|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1068.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1110.7|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1053.0|1168.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Black|1130.0|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|297.4|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||224.7|390.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|383.4|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||297.1|490.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|443.3|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||352.8|533.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|491.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||474.1|509.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|500.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|510.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||439.5|580.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|510.9|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||477.3|544.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|525.3|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|532.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|577.6|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||545.5|609.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|650.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||512.5|814.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|692.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||644.5|740.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|712.8|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||703.0|722.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|756.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|765.5|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic|847.7|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Hispanic||Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|352.2|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Multiracial|389.6|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||293.4|485.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|166.9|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||122.4|211.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|269.6|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Other includes Unknown; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||170.9|404.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|429.3|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||315.4|570.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|747.8|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other|1095.3|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||784.7|1522.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|Other||San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|541.3|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||520.5|563.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|610.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|628.6|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||584.2|672.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|637.7|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||627.1|648.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|731.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|736.0|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||720.4|751.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|741.9|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|747.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|749.3|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|753.2|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|754.3|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||734.3|774.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|759.4|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||738.0|780.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|768.1|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||752.0|784.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|773.8|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||755.1|792.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|780.0|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||758.1|802.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|789.5|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Both|White|1054.0|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1020.1|1088.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|448.0|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||427.1|470.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|490.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|502.4|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||492.5|512.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|565.3|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|594.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||562.9|625.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|619.0|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||603.5|634.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|624.2|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|633.6|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|634.9|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||618.7|651.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|636.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||612.1|660.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|661.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|686.1|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||663.5|709.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|712.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||695.8|729.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|715.3|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||697.9|732.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|719.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|819.8|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Female|All|853.1|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||820.2|886.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|654.6|Seattle, WA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||624.4|686.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|700.0|New York City, NY|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|NYC DOHMH Bureau of Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|700.5|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||687.0|714.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|791.9|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2015 mid-year population estimates||||749.9|834.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|850.3|Fort Worth (Tarrant County), TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|National Center for Health Statistics|||||827.9|872.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|859.2|Boston, MA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Population denominators based on extrapolation after year 2010|Rates include undetermined causes and missing causes of death||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|863.2|U.S. Total, U.S. Total|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Health, United States, 2016, HHS/CDC/NCHS, Table 17 https://www.cdc.gov/nchs/data/hus/2016/017.pdf||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|887.1|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder All ICD-10 codes|||||853.6|920.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|893.5|San Antonio, TX|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||871.6|915.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|937.6|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||906.0|970.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|965.4|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|984.4|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|1007.5|Las Vegas (Clark County), NV|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Nevada Vital Records - Clark County Deaths|||||985.2|1029.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|1066.7|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|1076.9|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||1051.7|1102.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|1236.1|Detroit, MI|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|2015 Michigan Death Certificate Registry.     Division for Vital Records & Health Statistics, Michigan Department of Health & Human Services; Population Estimate (latest update 9/2014), National Center for Health Statistics, U.S. Census Populations With Bridged Race Categories .||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2015|Male|All|1308.1|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1258.4|1357.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|594.2|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||586.1|602.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|675.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||650.5|700.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|712.9|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||693.8|731.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|784.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|793.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|818.1|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||799.3|837.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|844.4|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|880.2|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||865.8|894.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|All|1018.3|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||990.7|1045.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|849.1|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||701.0|997.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native|1943.4|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|394.6|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||350.9|438.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|394.7|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||375.8|413.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|433.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||394.3|471.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|441.4|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||328.1|584.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|468.5|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||411.7|525.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|542.9|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|684.3|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Asian/PI|969.6|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||767.6|1208.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|751.5|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||704.1|798.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|880.2|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||783.4|977.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|979.4|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||955.9|1002.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|982.3|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||939.6|1026.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|1008.4|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|1019.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|1024.3|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||965.7|1082.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|1072.8|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Black|1109.8|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1052.6|1167.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|380.4|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||296.7|483.6
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|470.4|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||379.7|561.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|507.9|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||490.2|525.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|547.0|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||474.0|619.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|598.8|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|623.3|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|630.1|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||489.3|798.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|696.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||649.5|743.4
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Hispanic|889.6|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|371.0|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||281.5|460.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Multiracial|510.0|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|240.2|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Other includes Unknown; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||150.5|363.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|433.9|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)||||321.0|573.7
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|735.5|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||544.1|972.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|Other|759.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|637.9|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||627.3|648.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|644.4|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||599.5|689.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|716.6|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|720.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||700.1|741.3
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|735.9|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|754.2|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|763.5|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||744.8|782.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|792.9|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||770.8|815.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Both|White|1006.8|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||973.6|1039.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|507.0|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||497.0|516.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|589.3|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||558.5|620.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|590.7|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||567.8|613.5
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|632.3|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|653.1|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|671.3|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||649.1|694.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|695.8|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|723.3|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||706.5|740.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Female|All|832.5|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||800.0|865.0
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|698.8|San Diego County, CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released December 2017. Data are from the Compressed Mortality File 1999-2016 Series 20 No. 2V, 2017, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Jan 12, 2018|All data was pulled using the CDC WONDER mortality online query system for the specified ICD10 codes.The populations used to calculate standard age-adjusted rates are documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#2000 Standard Population.The method used to calculate age-adjusted rates is documented here: More information:http://wonder.cdc.gov/wonder/help/ucd.html#Age-Adjusted Rates.||||685.5|712.2
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|778.9|Oakland (Alameda County), CA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Alameda County vital statistics files|Using 2016 mid-year population estimates||||738.1|819.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|861.3|Portland (Multnomah County), OR|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|CDC Wonder|||||828.7|893.9
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|958.9|Minneapolis, MN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Minnesota Vital Statistics||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|993.8|Denver, CO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from the Colorado Department of Public Health and Environment||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|1008.3|Indianapolis (Marion County), IN|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|MCPHD Death Certificate data|||||975.7|1041.8
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|1021.5|Kansas City, MO|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|||||||
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|1089.9|Philadelphia, PA|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|PA Eddie-->Vital Statistics|||||1064.8|1115.1
Life Expectancy and Death Rate (Overall)|All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)|2016|Male|All|1269.0|Columbus, OH|All deaths per 100,000 population using 2010 US Census figures, age adjusted to the year 2000 standard population|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census 2010 Decenial Census.  Analyzed by Columbus Public Health, Office of Epidemiology||Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||1220.0|1317.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|73.6|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|76.7|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|77.5|Washington, DC|Life expectancy at birth|DC DOH||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|78.1|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|78.3|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||78.1|78.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|78.7|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|80.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||79.5|80.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|80.9|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|81.7|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|All|82.1|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||81.7|82.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Asian/PI|83.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Asian/PI|86.2|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||84.8|87.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Asian/PI|86.4|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files|Only Asian (does not include Pacific Islanders)|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Asian/PI|86.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||85.8|87.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Asian/PI|88.4|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Asian/PI||New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for 2011-2012 were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. *Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|72.8|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|72.8|Washington, DC|Life expectancy at birth|DC DOH||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|72.9|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|73.2|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||72.1|74.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|74.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|75.3|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||73.8|76.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|77.1|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Black|77.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|81.9|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|82.5|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|83.1|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|84.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||82.2|85.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|84.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|84.4|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||82.1|86.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Hispanic|88.4|Washington, DC|Life expectancy at birth|DC DOH||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Other|93.3|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|Other||Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to standard error >2.0.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|73.4|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|76.6|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|78.5|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|80.8|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|81.4|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|82.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||81.1|82.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|82.2|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||81.8|82.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Both|White|84.1|Washington, DC|Life expectancy at birth|DC DOH||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|76.7|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|79.3|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|79.5|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|79.8|Washington, DC|Life expectancy at birth|DC DOH||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|80.4|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||80.1|80.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|80.5|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|80.9|San Antonio, TX|Life expectancy at birth|Institute for Health Metrics and Evaluation (IHME). United States Life Expectancy Estimates by County 1985-2010. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2013. Accessed 1/10/2018- http://ghdx.healthdata.org/record/united-states-life-expectancy-estimates-county-1985-2010||Bexar County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|81.0|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|83.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||82.2|83.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|83.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|83.6|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Female|All|84.3|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||83.8|84.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|70.4|Cleveland, OH|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|72.2|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|74.1|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|74.9|Washington, DC|Life expectancy at birth|DC DOH||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|75.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|75.9|San Antonio, TX|Life expectancy at birth|Institute for Health Metrics and Evaluation (IHME). United States Life Expectancy Estimates by County 1985-2010. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2013. Accessed 1/10/2018- http://ghdx.healthdata.org/record/united-states-life-expectancy-estimates-county-1985-2010||Bexar County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|76.0|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||75.7|76.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|76.2|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|76.8|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2010 mid-year population estimates||||76.1|77.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|78.1|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|79.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2010|Male|All|79.6|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||79.1|80.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|72.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2009-2011||||72.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|76.8|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|77.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|78.6|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||78.4|78.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|78.7|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|79.2|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||78.8|79.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|80.9|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|81.8|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|81.8|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|82.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|All|82.0|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||81.6|82.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|American Indian/Alaska Native|76.4|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|American Indian/Alaska Native|80.0|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query|American Indian alone|||76.3|83.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI|82.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI|85.2|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||84.2|86.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI|85.8|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI|86.2|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI|87.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files|Only Asian (does not include Pacific Islanders)|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI|88.0|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||86.4|89.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Asian/PI||New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for 2011-2012 were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. *Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|72.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2009-2011||||71.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|72.4|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|74.3|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|74.8|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||73.1|76.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|75.7|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|75.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||74.3|77.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|76.9|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|77.0|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Black|78.5|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|81.8|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|83.3|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|83.4|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|84.8|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|84.9|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|86.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||84.2|89.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Hispanic|87.8|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||85.4|90.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Multiracial|83.0|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|Other|91.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|75.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2009-2011||||74.0|76.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|76.4|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|78.8|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|78.9|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||78.5|79.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|81.0|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|81.1|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|81.1|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|81.4|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Both|White|82.2|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||81.7|82.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|76.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2009-2011||||75.0|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|78.9|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|79.6|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|80.0|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|80.7|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||80.4|80.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|81.1|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|81.6|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||81.1|82.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|83.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|84.0|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|84.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Female|All|84.5|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||83.9|85.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|68.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2009-2011||||67.0|69.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|71.9|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|73.7|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|75.7|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|76.3|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|76.4|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||76.1|76.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|76.7|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||76.2|77.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|78.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|79.4|Los Angeles, CA|Life expectancy at birth|Los Angeles County Health Department||County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|79.4|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||78.9|80.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2011|Male|All|79.6|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|69.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||68.3|69.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|73.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2010-2012||||72.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|75.1|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2010 to 2012.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||75.1|75.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|77.0|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|77.7|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||77.4|78.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|77.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|78.0|Chicago, Il|Life expectancy at birth|VCU Center on Society and Health (www.societyhealth.vcu.edu/maps)||Computed with range of years - 2003 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|78.6|Denver, CO|Life expectancy at birth|||Computed with range of years - 2007 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|78.6|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||78.4|78.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|78.8|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|79.5|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||79.2|79.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|80.1|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|81.1|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|81.9|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|82.0|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||81.6|82.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|All|82.8|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|American Indian/Alaska Native|78.4|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query|American Indian alone|||74.3|82.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|82.4|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|82.8|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|84.4|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||83.2|85.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|84.8|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||83.5|86.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|86.7|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group |||85.6|87.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|87.2|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI|87.5|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files|Only Asian (does not include Pacific Islanders)|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Asian/PI||Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).; Computed with range of years - 2010 to 2012.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|72.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2010-2012||||71.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|72.6|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||71.5|73.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|73.4|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|73.6|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||72.0|75.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|73.7|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2010 to 2012.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||73.6|73.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|73.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|74.9|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||74.3|75.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|77.0|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|77.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|77.1|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||75.4|78.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|77.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Black|79.4|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|82.1|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|82.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|83.1|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||81.3|84.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|84.9|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|85.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|86.4|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|86.6|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||84.5|88.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|87.5|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||85.1|89.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic|90.5|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||87.7|93.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Hispanic||Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).; Computed with range of years - 2010 to 2012.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Multiracial|76.0|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Other|93.3|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Other||Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).; Computed with range of years - 2010 to 2012.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|Other||Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to standard error >2.0.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|75.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2010-2012||||74.0|76.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|75.2|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2010 to 2012.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||75.2|75.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|76.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|78.4|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||78.0|78.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|78.6|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|79.4|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||79.0|79.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|79.5|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|80.8|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||79.9|81.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|81.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|81.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|81.7|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||81.2|82.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Both|White|82.8|Long Beach, CA|Life expectancy at birth|Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates; California Electronic Death Registration System (CA-EDRS), as of April 1, 2016; California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|76.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2010-2012||||76.0|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|77.7|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2010 to 2012.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||77.6|77.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|79.3|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|80.1|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|80.3|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|80.6|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||80.4|80.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|81.0|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||80.6|81.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|81.2|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|81.5|Denver, CO|Life expectancy at birth|||Computed with range of years - 2007 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|82.0|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||81.5|82.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|82.2|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||81.5|83.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|82.8|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|83.4|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|84.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Female|All|84.6|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||84.1|85.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|69.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2010-2012||||68.0|69.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|72.3|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|72.5|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2010 to 2012.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||72.4|72.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|73.5|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|74.2|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||73.8|74.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|75.7|Denver, CO|Life expectancy at birth|||Computed with range of years - 2007 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|75.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|76.4|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||76.2|76.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|76.4|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|76.5|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2011 mid-year population estimates||||75.7|77.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|77.0|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||76.5|77.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|77.1|Boston, MA|Life expectancy at birth|Boston Public Health Commission Research and Evaluation; Boston Resident Deaths, Massachusetts Department of Public Health|Abridged Life Table/Life Expectancy Methodology Used|Computed with range of years - 2008 to 2012||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|78.4|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics|Life expectancy at birth. For life expectancy computations, single-year age group populations were based on decennial census counts. Life expectancies for 2010 are calculated based on 2010 Census population. Population data for life expectancies for subsequent years were extrapolated based on single-year age groups of Census population, 2000 and 2010. Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates.  Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|79.2|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||78.6|79.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2012|Male|All|79.5|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|73.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2011-2013||||72.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|73.9|Baltimore, MD|Life expectancy at birth|||Computed with range of years - 2011 to 2013||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|77.4|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|77.7|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|77.9|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||77.5|78.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|78.8|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||78.6|78.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|78.8|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|79.2|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||78.8|79.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|79.4|San Antonio, TX|Life expectancy at birth|Bexar County||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|79.7|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||79.2|80.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|80.0|Miami (Miami-Dade County), FL|Life expectancy at birth|VCU Center on Society and Health (www.societyhealth.vcu.edu/maps)||Computed with range of years - 2003 to 2013||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|80.0|Phoenix, AZ|Life expectancy at birth|VCU||Maricopa County; Computed with range of years - 2004 to 2013||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|80.2|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|81.1|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|82.0|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|82.2|Seattle, WA|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|All|82.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Death Statistical Master Files||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|American Indian/Alaska Native|74.1|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query|American Indian alone|||70.4|77.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Asian/PI|81.5|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Asian/PI|84.9|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||83.7|86.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Asian/PI|85.1|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Asian/PI|86.5|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||85.2|87.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Asian/PI|87.5|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Asian/PI|88.7|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System |Only Asian (does not include Pacific Islanders)|2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|69.2|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|72.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2011-2013||||72.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|73.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||72.0|74.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|73.6|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|73.6|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|74.3|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||72.7|75.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|74.9|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||74.2|75.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|75.9|San Antonio, TX|Life expectancy at birth|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|76.2|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|77.1|Seattle, WA|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|77.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Black|77.6|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|79.0|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||77.5|80.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|79.3|San Antonio, TX|Life expectancy at birth|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|81.4|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|82.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|82.4|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|83.1|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||81.2|85.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|86.0|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|86.1|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|88.1|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||85.5|90.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Hispanic|91.5|Seattle, WA|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Other|90.7|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|Other||Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to standard error >2.0.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|76.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2011-2013||||75.0|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|76.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|78.7|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||78.3|79.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|79.0|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|79.2|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||78.8|79.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|79.5|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|79.9|San Antonio, TX|Life expectancy at birth|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|81.0|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|81.1|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|81.1|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|81.8|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||80.8|82.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Both|White|82.3|Seattle, WA|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|77.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2011-2013||||76.0|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|79.3|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|79.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|80.3|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|80.6|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||80.4|80.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|80.7|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||80.3|81.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|81.2|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|81.6|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||81.1|82.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|81.7|San Antonio, TX|Life expectancy at birth|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|82.4|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||81.7|83.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|83.0|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|83.4|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|84.4|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|84.5|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Female|All|84.9|Seattle, WA|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|69.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2011-2013||||68.0|69.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|73.2|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|74.2|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|74.8|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||74.3|75.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|75.5|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|76.4|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports, Volume 63, Number 7, United States Life Tables, 2010. Table 19. http://www.cdc.gov/nchs/data/hus/hus14.pdf#016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|76.7|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||76.5|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|76.7|Portland (Multnomah County), OR|Life expectancy at birth|Oregon Death Certificates, National Center for Health Statistics Population Estimates, Census Bureau Population Estimates (Vintage 2012)|OPHAT- Life Expectancy query||||76.2|77.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|76.8|San Antonio, TX|Life expectancy at birth|||Bexar County (Not just San Antonio)||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|76.9|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2012 mid-year population estimates||||76.1|77.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|77.3|Boston, MA|Life expectancy at birth|Boston Resident Deaths, MA Department of Public Health, US Census||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|78.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|79.4|Seattle, WA|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|79.7|San Francisco, CA|Life expectancy at birth|California Department of Public Health, death statistical master files, analysis by San Francisco Department of Health||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2013|Male|All|80.1|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|73.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2012-2014||||72.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|77.0|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|77.3|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|77.6|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||77.2|77.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|78.7|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||78.5|78.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|78.8|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports: Health, United States, 2015; Table 15 - Life expectancy at birth, at age 65, and at age 75, by sex, race, and Hispanic origin: United States, selected years 19002014||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|79.6|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||79.2|79.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|80.3|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth||||79.7|80.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|80.8|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|81.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|82.4|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|All|82.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||82.4|83.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|American Indian/Alaska Native|78.8|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||75.7|81.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Asian/PI|81.1|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Asian/PI|85.9|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||85.0|86.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Asian/PI|86.2|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||84.9|87.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Asian/PI|86.4|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth|Represents Asiain population alone. Does not include Pacific Islander population|||85.1|87.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Asian/PI|86.6|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System |Only Asian (does not include Pacific Islanders)|2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Asian/PI|88.5|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|72.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2012-2014||||72.0|73.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|72.8|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|73.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth||||71.9|74.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|73.5|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|74.4|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||73.7|75.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|75.5|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||74.0|76.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|77.2|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|77.3|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|77.5|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Black|78.6|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||77.2|80.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|80.7|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|82.6|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|83.3|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth||||81.5|85.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|83.3|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||81.5|85.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|84.6|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|85.5|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||83.5|87.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|87.2|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Hispanic|97.6|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||94.2|100.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Other|71.6|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||65.3|77.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|Other|90.1|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|76.6|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|77.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2012-2014||||76.0|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|78.4|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||78.0|78.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|79.1|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|79.6|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|79.6|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||79.3|80.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|81.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|81.6|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|82.4|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth||||81.5|83.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Both|White|82.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||82.4|83.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|77.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2012-2014||||76.0|77.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|79.5|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|79.6|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|79.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|80.1|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||79.7|80.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|80.7|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||80.5|80.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|81.2|San Antonio, TX|Life expectancy at birth|Institute for Health Metrics and Evaluation (IHME)- http://www.healthdata.org/sites/default/files/files/county_profiles/US/2015/County_Report_Bexar_County_Texas.pdf||Bexar County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|81.2|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports: Health, United States, 2015; Table 15 - Life expectancy at birth, at age 65, and at age 75, by sex, race, and Hispanic origin: United States, selected years 19002015||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|81.9|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||81.4|82.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|83.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth||||82.2|83.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|83.6|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|83.7|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|84.7|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Female|All|85.5|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||85.0|86.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|69.0|Detroit, MI|Life expectancy at birth|MDHHS, Department of Vital Records and Health Statistics|Life expectancy at birth; Life tables were calculated using the standard Chiang method. Computed with range of years 2012-2014||||68.0|69.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|73.0|Philadelphia, PA|Life expectancy at birth|Vital Statistics Report||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|74.2|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|74.8|Indianapolis (Marion County), IN|Life expectancy at birth|MCPHD Death Certificate data|||||74.3|75.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|74.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|76.0|San Antonio, TX|Life expectancy at birth|Institute for Health Metrics and Evaluation (IHME)- http://www.healthdata.org/sites/default/files/files/county_profiles/US/2015/County_Report_Bexar_County_Texas.pdf||Bexar County level data||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|76.4|U.S. Total, U.S. Total|Life expectancy at birth|National Vital Statistics Reports: Health, United States, 2015; Table 15 - Life expectancy at birth, at age 65, and at age 75, by sex, race, and Hispanic origin: United States, selected years 19002016||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|76.5|Fort Worth (Tarrant County), TX|Life expectancy at birth|Institute for Health Metrics and Evaluation, University of Washington|||||76.3|76.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|77.2|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT life expectancy query|||||76.7|77.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|77.5|Boston, MA|Life expectancy at birth|Boston resident deaths, Massachusetts Department of Public Health (data as of December 2016)||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|77.5|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County Vital Statistics|Life expectancy at birth||||76.7|78.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|78.5|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|79.9|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||79.4|80.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2014|Male|All|80.0|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|74.5|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2013 to 2015.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||74.5|74.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|76.8|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|77.3|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|79.3|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||79.0|79.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|79.7|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||79.1|80.2
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|80.0|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|81.2|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|82.1|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|All|83.4|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||83.1|83.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI|81.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI|84.8|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||83.7|86.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI|85.2|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||84.0|86.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI|86.9|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI|87.0|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||86.1|87.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI|87.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System |Only Asian (does not include Pacific Islanders)|2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Asian/PI||Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).; Computed with range of years - 2013 to 2015.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|72.1|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|72.8|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||71.8|73.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|73.2|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2013 to 2015.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||73.1|73.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|73.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|74.5|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||73.0|76.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|77.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|77.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|77.6|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Black|79.1|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||77.8|80.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|81.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|82.4|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|83.3|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|83.6|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||81.7|85.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|84.2|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|86.7|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||84.8|88.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic|96.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||93.7|99.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Hispanic||Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).; Computed with range of years - 2013 to 2015.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Other|72.5|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||68.5|76.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Other|81.5|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||78.5|84.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Other|92.8|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Other||Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235).; Computed with range of years - 2013 to 2015.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|Other||Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to standard error >2.0.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|74.6|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2013 to 2015.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||74.5|74.6
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|78.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|79.2|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|79.4|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||79.0|79.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|79.5|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|81.3|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|81.3|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|81.4|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||80.5|82.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Both|White|83.5|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||83.1|83.9
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|77.4|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2013 to 2015.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||77.3|77.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|79.4|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|79.7|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|81.7|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||81.2|82.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|82.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||81.3|82.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|82.8|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|83.5|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|84.2|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Female|All|85.8|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||85.3|86.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|71.6|Columbus, OH|Life expectancy at birth|Mortality data from Ohio Department of Health, Office of Vital Statistics.  Population files from US Census, American Community Survey, 5-year estimates, 2010, 2011, 2012, 2013, 2014, and 2015.  Analyzed by Columbus Public Health, Office of Epidemiology||Computed with range of years - 2013 to 2015.; Columbus defined based on resident zipcode within (43201,43202,43203,43204,43205,43206,43207,43209,43211,43214,43215,43219,43222,43223,43224,43227,43228,43229,43231,43232,43235)|||71.6|71.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|73.9|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|74.9|Las Vegas (Clark County), NV|Life expectancy at birth|Nevada Vital Records - Clark County Deaths||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|76.9|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||76.5|77.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|77.0|Boston, MA|Life expectancy at birth|Boston Resident Deaths, Massachusetts Department of Public Health (data as of December 2016)|Chiang (II) Life Expectancy Methodology|||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|77.3|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2015 mid-year population estimates||||76.5|78.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|78.6|New York City, NY|Life expectancy at birth|NYC DOHMH Bureau of Vital Statistics||Life expectancy for Asians and Pacific Islanders is not displayed because the required single year of age population denominators are too small to produce reliable estimates. Life expectancy calculations use national data from the NCHS, including deaths to New York City residents that occurred outside of New York City.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|79.8|San Diego County, CA|Life expectancy at birth|California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System ||2013-2015 VRBIS data does not include out of state resident deaths||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2015|Male|All|80.9|Seattle, WA|Life expectancy at birth|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||80.3|81.4
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|All|77.0|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|All|79.7|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||79.4|80.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|All|80.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||79.5|80.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Asian/PI|85.9|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||84.6|87.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Asian/PI|86.0|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||84.8|87.3
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Black|72.5|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Black|72.9|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||71.9|74.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Black|76.7|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||75.2|78.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Hispanic|83.4|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||81.3|85.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Hispanic|86.4|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||84.3|88.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Other|78.5|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||75.3|81.7
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|Other||Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates; 'Other' includes all other races including American Indian/Alaskan Native, multirace, and other races)|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to standard error >2.0.||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|White|79.3|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|White|80.1|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||79.8|80.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Both|White|81.1|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||80.2|82.1
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Female|All|79.7|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Female|All|82.3|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||81.6|83.0
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Female|All|82.4|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||82.0|82.8
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Male|All|74.3|Kansas City, MO|Life expectancy at birth|||||||
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Male|All|77.1|Portland (Multnomah County), OR|Life expectancy at birth|OPHAT Life Expectancy Query|||||76.6|77.5
Life Expectancy and Death Rate (Overall)|Life Expectancy at Birth (Years)|2016|Male|All|77.5|Oakland (Alameda County), CA|Life expectancy at birth|Alameda County vital statistics files|Using 2016 mid-year population estimates||||76.8|78.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|3.2|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||2.0|4.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|4.0|Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|4.3|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|4.4|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|4.5|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|4.6|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|5.2|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.4|6.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.0|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.1|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2010-2012||Value is for range of years 2010-2012|||4.9|7.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.1|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.2|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.4|Houston, TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 20, 2015.||Harris County data, not just Houston||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.6|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||5.6|7.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.7|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|6.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|7.4|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|7.5|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|8.0|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|All|9.5|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.5|11.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|American Indian/Alaska Native|1.5|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|American Indian/Alaska Native|7.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010; American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|American Indian/Alaska Native|8.3|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|0.0|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|1.6|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|2.0|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|2.4|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|2.7|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|2.7|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|3.1|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|4.2|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI|4.3|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI||San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Asian/PI||Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Does not include Pacific Islanders as we report data separately for this group.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|7.7|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|8.0|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|8.4|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|9.2|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|9.8|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|10.1|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|10.7|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|10.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|11.1|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||7.5|14.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|11.3|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2010-2012||Value is for range of years 2010-2012|||8.1|15.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|11.5|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|11.6|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|12.1|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|12.4|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.8|16.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|13.9|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|14.5|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||8.7|20.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|14.8|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|16.6|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black|21.0|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Black||Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|2.7|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|3.7|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|4.5|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2010-2012||Value is for range of years 2010-2012|||2.9|6.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|4.6|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|4.6|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|4.8|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.5|6.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|5.3|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|5.6|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|5.9|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|5.9|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|6.4|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||5.2|7.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|6.9|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|7.3|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|8.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic|8.9|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Hispanic||Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Multiracial|7.9|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Multiracial|12.1|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other|1.0|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other|3.2|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other|3.9|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other|12.2|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other|14.3|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other||San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|Other||Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.||Includes multi-race; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed here to protect confidentiality.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|2.0|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2016, October 2017.|||||0.9|3.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|2.9|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|3.4|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2008-2010||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|3.6|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|3.7|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2010-2012||Value is for range of years 2010-2012|||2.0|6.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|3.8|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|4.0|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||2.8|5.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|4.3|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|4.6|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|4.6|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|4.9|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|4.9|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|5.2|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||3.4|7.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|5.2|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|5.5|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|6.0|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|6.2|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White|7.5|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.0|10.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2010|Both|White||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|3.5|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|3.7|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||2.6|5.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.3|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.6|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.7|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.7|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.8|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.8|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|4.9|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|5.3|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.4|6.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|5.6|Houston, TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 20, 2015.||Harris County data, not just Houston||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|5.8|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|5.9|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|5.9|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2011-2013||Value is for range of years 2011-2013|||4.8|7.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|6.1|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|6.2|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|7.1|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|7.4|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|7.7|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|8.2|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|9.3|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|10.5|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|11.7|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.5|13.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|All|12.6|Detroit, MI|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|American Indian/Alaska Native|2.1|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011; American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|American Indian/Alaska Native|3.0|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|American Indian/Alaska Native|8.2|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|0.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|1.5|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|2.8|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|2.9|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|3.0|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|3.0|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|3.4|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|4.0|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2011-2013||Value is for range of years 2011-2013|||1.9|7.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|4.4|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|5.7|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI|6.1|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|5.4|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|6.1|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|7.1|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.3|9.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|7.5|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|7.8|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|8.1|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|8.6|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|8.8|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|9.9|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|10.2|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|10.4|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|11.2|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|11.5|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|11.6|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|13.2|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2011-2013||Value is for range of years 2011-2013|||9.7|17.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|14.1|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|14.3|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|14.3|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|14.6|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|14.8|Detroit, MI|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|15.2|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black|17.3|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||13.1|21.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Black||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|3.2|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|3.7|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2011-2013||Value is for range of years 2011-2013|||2.2|5.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|3.7|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|4.5|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|4.6|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|4.8|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|4.9|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|5.1|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|5.2|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|5.2|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|5.3|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|5.7|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.2|7.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|6.1|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|6.2|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|7.1|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|7.5|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|7.9|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic|8.3|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Multiracial|7.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Multiracial|12.3|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other|1.9|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other|3.0|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other|4.2|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other|4.3|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other|11.5|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other|16.7|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|1.8|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|2.5|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|2.7|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||1.5|4.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.0|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.1|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.2|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.4|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2009-2011||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.5|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.7|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.7|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2011-2013||Value is for range of years 2011-2013|||2.0|6.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|3.7|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|4.0|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|4.2|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|4.2|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|4.7|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|4.8|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.4|6.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|5.1|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|All Infant deaths (per 1,000 live births, <1 year), Linked Birth/Infant Death Data Set, CDC/NCHS||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|5.4|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|5.4|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|6.2|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|9.2|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|9.9|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.9|12.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White|10.1|Detroit, MI|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Both|White||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Female|All|3.5|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||2.0|5.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2011|Male|All|3.7|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||2.2|5.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|3.2|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|3.9|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|4.2|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|4.3|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|4.7|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|4.7|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|4.9|Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|5.0|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|5.1|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||3.8|6.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|5.2|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.3|6.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|5.5|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|5.6|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|5.9|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2010-2012||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|6.0|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|6.1|Houston, TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 20, 2015.||Harris County data, not just Houston||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|6.1|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2012-2014||Value is for range of years 2012-2014|||4.9|7.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|6.4|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|6.4|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|6.9|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|7.1|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|7.4|Chicago, Il|Infant deaths per 1,000 live births.|US DHHS, CDC, NCHS, Division of Vital Statistics. Linked Birth / Infant Death Records 2007-2013|ICD-10 codes: All deaths for under 3|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|7.9|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|8.8|Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data|||||7.3|10.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|9.7|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|10.1|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|10.2|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.2|12.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|All|14.7|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||12.4|17.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|American Indian/Alaska Native|2.9|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|0.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2010-2012||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|1.1|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|2.6|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|2.7|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|2.9|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|3.3|Chicago, Il|Infant deaths per 1,000 live births.|US DHHS, CDC, NCHS, Division of Vital Statistics. Linked Birth / Infant Death Records 2007-2013|ICD-10 codes: All deaths for under 3|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|3.3|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|6.1|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI|7.1|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Asian/PI||Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2012-2014||Value is for range of years 2012-2014; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to count less than 10.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|5.7|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|5.8|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|6.2|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|8.2|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||5.2|11.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|8.5|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|8.7|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|9.5|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|9.7|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|9.9|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|10.2|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|10.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2010-2012||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|11.2|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|11.4|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|12.3|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|12.7|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|12.7|Chicago, Il|Infant deaths per 1,000 live births.|US DHHS, CDC, NCHS, Division of Vital Statistics. Linked Birth / Infant Death Records 2007-2013|ICD-10 codes: All deaths for under 2|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|12.9|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|13.8|Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data|||||10.3|18.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|14.3|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.5|18.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|15.5|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2012-2014||Value is for range of years 2012-2014|||11.6|20.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|15.6|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|15.7|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|16.0|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||13.3|18.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black|16.7|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Black||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|3.3|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|3.5|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|4.2|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|4.3|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2012-2014||Value is for range of years 2012-2014|||2.7|6.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|4.4|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.1|5.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|4.6|Long Beach, CA|Infant deaths per 1,000 live births.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Data for the race/ethnicity groups of White and Two or More Races missing.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|5.1|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|5.2|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|5.5|Chicago, Il|Infant deaths per 1,000 live births.|US DHHS, CDC, NCHS, Division of Vital Statistics. Linked Birth / Infant Death Records 2007-2013|ICD-10 codes: All deaths for under 1|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|6.4|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|6.7|Philadelphia, PA|Infant deaths per 1,000 live births.|Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|7.2|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|7.2|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|7.7|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic|10.9|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Multiracial|8.2|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Multiracial|8.7|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2010-2012||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other|1.4|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other|1.9|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other|2.4|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other|4.1|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other|10.7|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other|17.9|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|Other||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|1.9|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|2.7|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|2.7|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|2.8|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2012-2014||Value is for range of years 2012-2014|||1.4|5.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|3.1|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Data includes 2010-2012.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|3.3|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|3.4|Washington, DC|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|3.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2010-2012||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|3.7|Chicago, Il|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|3.7|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|4.1|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|4.4|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|4.7|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|4.7|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.3|6.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|4.7|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|5.0|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|5.1|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_09.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|5.3|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||3.6|7.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|5.9|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|7.9|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.3|10.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|11.8|Cleveland, OH|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White|15.7|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||6.9|24.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Both|White||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Female|All|4.6|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||2.8|7.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2012|Male|All|5.6|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||3.7|8.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|4.5|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|4.6|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|4.8|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.0|5.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|4.8|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||3.5|6.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|4.9|Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|5.0|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2013|||||4.4|5.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|5.2|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|5.6|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2013-2015||Value is for range of years 2013-2015|||4.6|6.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|5.6|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|5.9|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|6.0|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|6.0|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|6.6|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|7.0|Houston, TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services, Center for Health Statistics, 2015. Available at http://soupfin.tdh.state.tx.us/death10.htm.  Accessed May 20, 2015.||Harris County data, not just Houston||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|7.0|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2010-2012||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|7.1|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||6.1|8.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|7.3|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|9.4|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|9.9|Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data|||||8.2|11.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|10.3|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|10.6|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.6|12.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|All|13.1|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||10.9|15.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|American Indian/Alaska Native|4.9|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2011-2013; American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|2.2|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2011-2013||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|2.4|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|3.1|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|3.1|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|3.4|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2013|||||1.6|5.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|6.5|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI|9.0|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Asian/PI||Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2013-2015||Value is for range of years 2013-2015; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to count less than 10.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|6.6|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|8.1|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|8.5|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|8.6|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|9.2|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|10.2|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2011-2013||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|10.8|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|11.2|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|12.2|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||8.6|15.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|12.5|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|12.6|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|12.8|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||9.5|16.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|13.9|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2013-2015||Value is for range of years 2013-2015|||10.1|18.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|13.9|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|14.4|Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data|||||10.8|18.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|14.5|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.8|18.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|14.5|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||11.9|17.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black|15.9|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Black||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|2.7|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||1.7|3.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|2.8|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|3.9|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|4.4|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|4.6|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2013-2015||Value is for range of years 2013-2015|||3.0|6.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|4.9|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2013|||||4.1|5.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|4.9|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|5.7|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|6.5|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|6.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2011-2013||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|7.8|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic|8.1|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||6.3|9.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Multiracial|8.0|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2011-2013||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other|2.5|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2013|||||0.3|4.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other|5.1|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other|8.4|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other|11.8|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other||Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|Other||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|3.0|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|3.2|San Jose, CA|Infant deaths per 1,000 live births.|Santa Clara County, California Department of Public Health, 2010-2012 Vital Statistics|Three-year moving average rates are provided. 2013 (2011-2013)/2012(2010-2012)/2011(2009-2011)|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|3.4|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Linked Birth-Infant Death Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|3.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2011-2013||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.1|Phoenix, AZ|Infant deaths per 1,000 live births.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.2|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||3.0|5.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.2|San Antonio, TX|Infant deaths per 1,000 live births.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.3|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||2.8|62.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.4|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.5|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.1|5.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.6|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.6|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.9|Denver, CO|Infant deaths per 1,000 live births.|Vital stats|Death data not available for 2014. Multiple race category not available for death data. Some race categories could include persons w/ multiple races.|||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|4.9|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|5.1|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|7.2|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||4.8|9.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|7.7|Baltimore, MD|Infant deaths per 1,000 live births.|Maryland Annual Vital Statistics Reports (http://dhmh.maryland.gov/vsa/sitepages/reports.aspx)||Race/ethnicity is that of the mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White|10.0|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||3.1|16.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Both|White||Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2013-2015||Value is for range of years 2013-2015; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to count less than 10.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Female|All|4.3|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Female|All|4.6|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||2.8|7.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Male|All|4.8|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2013|Male|All|5.0|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|Oregon Linked Birth & Death Certificates, Oregon Birth Certificates|OPHAT- Infant Mortality query||||3.2|7.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|3.2|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2015 August 2016.|||||2.1|4.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|3.8|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014, and Vital Records Business Intelligence System, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded. This may affect comparability of statistics.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|4.3|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||4.2|4.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|4.4|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|4.6|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|5.1|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.2|5.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|5.1|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2014-2016||Value is for range of years 2014-2016|||4.1|6.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|5.1|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT infant mortality query|||||3.7|6.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|5.2|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||4.4|6.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|5.8|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|6.1|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|6.2|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|6.4|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2012-2014||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|6.9|Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|7.2|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||6.2|8.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|7.9|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||6.7|9.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|9.4|Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data|||||7.8|11.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|10.0|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.1|12.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|All|11.8|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||9.7|13.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|American Indian/Alaska Native|7.6|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2012-2014||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT infant mortality query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI|1.9|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014, and Vital Records Business Intelligence System, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Includes Asian only; Starting in 2014, deaths (and births) that occurred outside California are excluded. This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI|2.6|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI|3.4|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||2.8|3.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI|5.2|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2012-2014||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Denver, CO|Infant deaths per 1,000 live births.|Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <4.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2014-2016||Value is for range of years 2014-2016; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to count less than 10.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT infant mortality query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Asian/PI||San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|7.2|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.6|9.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|7.5|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|7.7|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||5.1|10.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|8.1|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|8.2|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2015 August 2016.|||||3.3|16.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|9.1|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|10.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2012-2014||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|11.1|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|11.3|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||6.6|16.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|11.6|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|11.7|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2014-2016||Value is for range of years 2014-2016|||8.2|16.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|12.1|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||9.9|14.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|13.1|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT infant mortality query|||||6.3|24.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|13.3|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||10.8|15.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|13.4|Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|13.6|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||10.3|16.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|14.0|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|14.8|Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data|||||11.3|19.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Black|15.0|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||11.2|18.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|3.7|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|4.0|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|4.0|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||3.8|4.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|4.0|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014, and Vital Records Business Intelligence System, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded. This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|4.5|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.2|5.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|4.7|Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2014-2016||Value is for range of years 2014-2016|||3.0|6.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|4.9|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|5.0|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||3.9|6.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|5.6|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||3.2|7.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|6.7|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||5.1|8.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic|7.9|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2012-2014||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT infant mortality query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Hispanic||Seattle, WA|Infant deaths per 1,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Multiracial|6.4|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Department of Health, Vital Records|Rate per 1,000 live births; 3-year rolling average|2012-2014; American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other|7.1|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||6.0|8.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||Denver, CO|Infant deaths per 1,000 live births.|Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <4.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|Other||San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|2.0|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2015 August 2016.|||||0.9|3.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|2.4|Miami (Miami-Dade County), FL|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|2.6|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|2.7|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|3.0|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|3.7|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||3.3|4.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|3.9|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||2.4|5.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|4.1|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014, and Vital Records Business Intelligence System, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded. This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|4.5|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT infant mortality query|||||3.1|6.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|4.8|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.5|6.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|4.9|U.S. Total, U.S. Total|Infant deaths per 1,000 live births.|Table 20 - http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|5.0|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||3.7|6.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|7.2|Detroit, MI|Infant deaths per 1,000 live births.|MDHHS|||||1.5|12.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White|7.3|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||4.7|9.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White||Indianapolis (Marion County), IN|Infant deaths per 1,000 live births.|MCPHD Death Certificate data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Both|White||Oakland (Alameda County), CA|Infant deaths per 1,000 live births.|Alameda County Vital Statistics Files, 2014-2016||Value is for range of years 2014-2016; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the indicator is suppressed due to count less than 10.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Female|All||Seattle, WA|Infant deaths per 1,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2014|Male|All||Seattle, WA|Infant deaths per 1,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|3.6|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015 and Vital Records Business Intelligence System, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded. This may affect comparability of statistics.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|4.2|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||4.0|4.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|4.3|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|5.2|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||4.3|6.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|5.3|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|5.4|Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|5.5|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|6.2|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||5.3|7.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|6.4|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|6.6|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||5.7|7.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|7.1|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|8.3|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||7.1|9.5
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|All|9.9|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.0|11.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|American Indian/Alaska Native|10.7|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016; American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI|2.2|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015 and Vital Records Business Intelligence System, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Includes Asian only; Starting in 2014, deaths (and births) that occurred outside California are excluded. This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI|2.6|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI|3.4|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||2.8|3.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI|3.9|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||1.3|9.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI|5.2|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI|8.8|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Asian/PI||San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|7.3|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015 and Vital Records Business Intelligence System, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded.; This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|7.9|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.9|17.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|8.0|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|8.3|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|9.5|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||6.5|12.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|9.6|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||6.9|12.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|10.1|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||9.2|11.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|10.3|Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|11.3|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|11.9|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|13.0|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||10.7|15.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|13.1|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.5|16.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black|14.0|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||8.7|19.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Black||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|3.9|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||3.7|4.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|4.1|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015 and Vital Records Business Intelligence System, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded.; This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|4.6|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||3.3|5.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|4.6|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|6.1|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||3.7|8.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|6.3|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|6.6|Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services|||||5.0|8.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|6.7|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||5.5|7.9
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic|7.7|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Hispanic||Seattle, WA|Infant deaths per 1,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Multiracial|9.2|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other|1.8|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other|6.6|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||5.5|7.7
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Suppressed due to total count <5||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other||Fort Worth (Tarrant County), TX|Infant deaths per 1,000 live births.|Texas Department of State Health Services||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. ||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|Other||San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|2.0|Seattle, WA|Infant deaths per 1,000 live births.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.9|3.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|2.5|Los Angeles, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||2.2|2.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|2.7|New York City, NY|Infant deaths per 1,000 live births.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|2.8|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|3.5|San Diego County, CA|Infant deaths per 1,000 live births.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015 and Vital Records Business Intelligence System, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Number of deaths of infants (<1 year) per 1,000 live births|Starting in 2014, deaths (and births) that occurred outside California are excluded.; This may affect comparability of statistics.; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|3.6|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|3.6|Philadelphia, PA|Infant deaths per 1,000 live births.|PA Eddie-->Vital Statistics|||||2.2|5.1
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|3.9|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|4.1|Las Vegas (Clark County), NV|Infant deaths per 1,000 live births.|CDC WONDER|||||2.8|5.4
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|5.1|San Antonio, TX|Infant deaths per 1,000 live births.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||By the race of the mother; Bexar County level data|||3.5|6.8
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White|7.7|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.1|10.3
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White||Boston, MA|Infant deaths per 1,000 live births.|Massachusetts linked infant birth-infant death file (death cohort), Massachusetts Department of Public Health (data as of February 2017)||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Both|White||Charlotte, NC|Infant deaths per 1,000 live births.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Female|All|6.6|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Female|All||Seattle, WA|Infant deaths per 1,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Male|All|7.6|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2013-2015||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2015|Male|All||Seattle, WA|Infant deaths per 1,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|All|4.5|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT Infant Mortality Query|||||3.3|6.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|All|4.6|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|All|6.7|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|All|6.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|All|10.3|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.3|12.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|American Indian/Alaska Native|14.2|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016; American Indian alone||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Asian/PI|6.8|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Asian/PI||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Asian/PI||Denver, CO|Infant deaths per 1,000 live births.|Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <4.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT Infant Mortality Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Black|4.4|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Black|10.9|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Black|11.6|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Black|15.2|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||11.4|19.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Black||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT Infant Mortality Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Hispanic|5.5|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Hispanic|5.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Hispanic||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Hispanic||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT Infant Mortality Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Other||Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <20.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Other||Denver, CO|Infant deaths per 1,000 live births.|Vital Records||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to total count <4.||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|Other||Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT Infant Mortality Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|White|3.5|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|White|3.7|Denver, CO|Infant deaths per 1,000 live births.|Vital Records||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|White|4.2|Portland (Multnomah County), OR|Infant deaths per 1,000 live births.|OPHAT Infant Mortality Query|||||2.7|6.2
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|White|4.3|Kansas City, MO|Infant deaths per 1,000 live births.|||||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Both|White|6.2|Columbus, OH|Infant deaths per 1,000 live births.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||3.8|8.6
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Female|All|6.7|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Infant Mortality Rate (Per 1,000 live births)|2016|Male|All|6.9|Minneapolis, MN|Infant deaths per 1,000 live births.|Minnesota Vital Statistics|Infant deaths per 1,000 live births, 3-year rolling average|2014-2016||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|1.6|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|6.5|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|6.5|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||5.9|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|7.3|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|7.6|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.0|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.1|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.3|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||8.2|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.4|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.4|Long Beach, CA|Percentage of births under 2,500 grams.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.6|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2010|||||7.9|9.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|8.8|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||8.2|9.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|9.1|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|9.4|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||9.0|9.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|9.6|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|9.9|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|10.2|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|10.9|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|All|11.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.5|11.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|American Indian/Alaska Native|6.3|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|American Indian/Alaska Native|7.1|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|American Indian/Alaska Native|7.6|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|American Indian/Alaska Native|9.4|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|7.1|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|7.4|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|8.0|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|8.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.4|11.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|8.5|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|8.6|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|8.9|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|9.1|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2010|||||7.1|11.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|9.2|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|9.5|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||9.1|9.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|9.5|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|9.6|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|11.7|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||9.6|14.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI|12.8|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||10.6|15.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Asian/PI||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|3.1|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|7.6|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||5.7|10.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|10.6|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|12.2|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|12.2|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|12.2|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|12.4|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|13.3|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|13.3|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|13.4|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2010|||||11.4|15.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|13.5|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|13.8|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||12.7|15.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|13.8|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|14.2|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|14.3|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||13.8|14.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|14.4|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|14.6|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||12.4|16.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|14.7|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|14.7|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Black|15.9|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|1.2|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|5.3|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||3.6|7.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|5.4|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|5.7|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|6.1|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|6.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|6.6|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|6.6|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2010|||||5.5|7.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|6.7|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|6.9|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|7.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|7.1|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|7.2|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|7.4|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|7.5|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||6.7|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|7.6|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.8|9.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|8.6|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|8.8|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|9.4|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.9|9.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Hispanic|10.4|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Multiracial|8.3|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|1.8|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|6.4|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.||Includes multi-race|||3.6|10.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|7.5|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|8.0|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|8.6|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|9.1|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|9.5|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|10.7|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|11.0|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|11.3|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||6.3|16.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other|13.3|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||10.0|16.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|Other||San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|1.2|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|5.5|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.1|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.2|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2010|||||4.9|7.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.3|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.3|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||5.6|7.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.5|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.8|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|6.9|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.3|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.4|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.4|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.5|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.6|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.8|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.7|8.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.8|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||7.2|8.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|7.9|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|8.2|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||7.3|9.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|9.0|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2010|Both|White|9.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.7|10.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|1.6|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|5.9|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|6.0|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.5|6.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|7.0|Long Beach, CA|Percentage of births under 2,500 grams.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|7.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|7.1|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|7.2|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|7.4|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2011|||||6.7|8.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|7.5|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|8.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|8.2|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|8.3|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||8.2|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|8.7|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|8.9|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||8.4|9.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|8.9|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|9.2|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|9.6|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|9.7|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|10.5|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|11.3|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|11.6|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|All|13.0|Detroit, MI|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|American Indian/Alaska Native|5.8|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|American Indian/Alaska Native|7.5|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|American Indian/Alaska Native|8.4|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|American Indian/Alaska Native|8.5|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|American Indian/Alaska Native|12.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|7.3|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|7.5|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2011|||||5.7|9.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|7.5|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|7.6|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|7.7|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|7.9|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|8.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|8.2|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|8.4|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||6.6|10.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|8.4|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|9.5|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|9.9|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||9.5|10.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|10.2|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|10.5|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|12.3|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.7|15.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Asian/PI|14.6|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||12.2|17.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|2.9|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|8.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|8.5|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|10.3|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|10.8|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2011|||||8.9|12.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|11.2|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|11.3|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||9.1|14.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|11.8|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|12.0|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|12.6|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|12.8|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||12.4|13.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|13.0|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||12.0|14.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|13.3|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||11.0|15.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|13.3|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|13.4|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|13.5|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|13.7|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|14.0|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|14.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|14.1|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|14.1|Detroit, MI|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|14.6|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|14.8|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Black|15.8|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|1.4|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|4.1|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|5.3|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.0|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.2|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.0|7.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.6|Detroit, MI|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.7|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2011|||||5.5|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.7|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.8|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.8|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|6.9|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|7.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|7.1|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.0|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|7.2|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.5|8.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|7.3|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|7.4|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|7.7|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|8.0|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||7.1|8.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|8.2|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|8.2|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|9.1|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Hispanic|10.9|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Multiracial|7.2|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Multiracial|7.4|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Multiracial|7.4|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Multiracial|8.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|1.7|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|6.3|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|7.6|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|9.2|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|9.9|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|10.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|10.4|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||4.8|16.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|10.5|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|10.9|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|11.2|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|12.8|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||9.3|16.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|14.5|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other|17.2|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Native Hawaiian and Other Pacific Islander||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|Other||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|1.2|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|4.9|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|5.0|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||4.4|5.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|5.4|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|5.4|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2011|||||4.1|7.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|5.6|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|6.2|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|6.3|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|6.7|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|6.7|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|6.8|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.0|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.2|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.2|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.2|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|7.6|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.5|7.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|8.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|8.1|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|8.3|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||7.4|9.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|8.3|Detroit, MI|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|9.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.3|10.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2011|Both|White|9.9|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|1.6|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|6.0|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|6.3|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.8|6.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|6.4|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|6.9|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|7.0|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|7.0|San Francisco, CA|Percentage of births under 2,500 grams.||||||6.5|7.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|7.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|7.5|Long Beach, CA|Percentage of births under 2,500 grams.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|7.7|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2012|||||6.9|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.0|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.2|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.2|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||8.1|8.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.3|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.4|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.4|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|8.8|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|9.0|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|9.4|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||8.9|9.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|9.7|Chicago, Il|Percentage of births under 2,500 grams.|IDPH Vital Statistics|Percent of births that are low birthweight (< 2500 grams or 5 lbs, 8 oz)|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|9.7|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|10.1|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|10.3|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|10.6|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.0|11.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|11.8|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|All|13.7|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|American Indian/Alaska Native|6.1|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|American Indian/Alaska Native|6.8|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|American Indian/Alaska Native|10.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|American Indian/Alaska Native|10.9|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|American Indian/Alaska Native|14.3|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|6.3|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|6.5|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|6.8|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.0|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.0|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2012|||||5.3|9.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.3|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.4|San Francisco, CA|Percentage of births under 2,500 grams.||||||6.5|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.7|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||6.0|9.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.7|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|7.8|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|8.1|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|8.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|8.3|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|9.0|Chicago, Il|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|11.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.8|14.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|11.3|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|11.3|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||10.9|11.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|11.5|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Asian/PI|14.0|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|3.3|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|8.2|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|9.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|9.8|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|10.2|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||8.0|12.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|10.5|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|11.9|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|12.0|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|12.0|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2012|||||10.1|14.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|12.1|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|12.8|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|12.9|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||11.8|14.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|13.0|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|13.2|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|13.2|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|13.4|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|13.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||12.4|14.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|13.5|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||13.1|14.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|14.2|Chicago, Il|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|14.2|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|14.6|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|14.7|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|14.7|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|15.2|San Francisco, CA|Percentage of births under 2,500 grams.||||||12.1|18.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Black|16.1|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|1.2|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|4.5|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|5.5|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|5.8|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|Hispanic Ancestry includes all races||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|5.8|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||4.6|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|5.9|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|5.9|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|6.3|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|6.6|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2012|||||5.4|7.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|6.6|San Francisco, CA|Percentage of births under 2,500 grams.||||||5.5|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|6.7|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.1|8.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|6.7|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||6.6|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|6.8|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|7.0|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||6.0|8.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|7.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|7.4|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|7.6|Chicago, Il|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|7.9|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|8.5|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|8.6|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|9.1|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|9.5|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Hispanic|13.1|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Multiracial|6.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Multiracial|7.8|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Multiracial|8.3|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Multiracial|11.1|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Multiracial|12.5|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|1.2|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|5.3|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|6.5|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|6.7|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||5.0|8.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|8.3|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|9.1|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|9.2|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|9.2|San Francisco, CA|Percentage of births under 2,500 grams.||||||6.5|11.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|9.4|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|9.7|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|10.7|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|12.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||8.6|15.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other|16.1|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|Other||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|1.3|Houston, TX|Percentage of births under 2,500 grams.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.||Harris County data, not just Houston||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|5.1|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|5.6|San Diego County, CA|Percentage of births under 2,500 grams.|Source: State of CA, Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010?2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services||Data includes 2010-2012. Births with unknown birth weight are not included.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|5.7|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2012|||||4.4|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|5.7|San Francisco, CA|Percentage of births under 2,500 grams.||||||5.0|6.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.2|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.4|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.5|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.5|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.5|Washington, DC|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.8|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.8|Philadelphia, PA|Percentage of births under 2,500 grams.|Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|6.9|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Low birth weight infants (percent, <2,500 grams), National Vital Statistics System-Natality (NVSS-N), CDC/NCHS||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.1|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.0|7.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.2|Chicago, Il|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.2|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.3|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.4|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.4|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|7.9|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|8.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|8.4|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||7.7|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|8.9|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.1|9.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|9.6|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2012|Both|White|11.4|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|6.4|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.9|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|6.5|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|6.7|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|6.9|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2013|||||6.2|7.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|7.0|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|7.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|7.3|San Francisco, CA|Percentage of births under 2,500 grams.||||||6.8|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|7.4|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.1|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||7.8|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.1|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||8.0|8.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.2|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.5|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.5|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.6|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|8.8|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|9.0|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|9.4|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|10.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.9|11.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|10.7|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||10.3|11.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|11.9|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|All|13.0|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|0.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|7.4|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|7.5|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702020||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|9.3|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|11.8|Cleveland, OH|Percentage of births under 2,500 grams.|||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|12.9|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|American Indian/Alaska Native|13.8|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|5.1|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|6.7|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|6.8|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||4.2|9.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|7.8|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|8.0|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|8.0|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2013|||||6.1|10.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|8.2|San Francisco, CA|Percentage of births under 2,500 grams.||||||7.2|9.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|8.3|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||6.5|10.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|8.3|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702022||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|9.1|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|9.2|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|9.3|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|9.7|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||9.3|10.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|10.3|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|12.6|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Asian/PI|12.9|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|7.7|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|9.8|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2013|||||8.0|11.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|10.4|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|11.0|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||8.7|13.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|11.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|11.4|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|12.1|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|12.3|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|12.6|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|12.7|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||12.3|13.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|12.8|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.0|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.1|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||12.2|14.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702018||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.4|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.4|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||12.4|14.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.6|San Francisco, CA|Percentage of births under 2,500 grams.||||||10.7|17.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|13.7|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||12.9|14.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|14.1|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|14.5|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|15.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|16.0|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Black|17.7|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|5.8|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2013|||||4.7|7.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|5.9|San Francisco, CA|Percentage of births under 2,500 grams.||||||4.9|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.3|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.5|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|Hispanic Ancestry includes all races||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.5|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.2|8.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.7|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|6.9|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.4|7.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.0|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.1|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702024||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.4|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.7|9.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.8|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.8|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|7.9|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||6.8|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|8.0|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|8.2|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|8.5|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|8.6|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|8.7|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|9.0|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||8.0|9.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Hispanic|12.3|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Multiracial|6.2|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Multiracial|7.7|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Multiracial|8.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Multiracial|10.0|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Multiracial|11.7|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||9.6|13.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Multiracial|12.5|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|5.8|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|6.8|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||5.3|8.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|7.2|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|7.5|San Francisco, CA|Percentage of births under 2,500 grams.||||||4.9|10.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|8.6|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||7.4|9.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|8.7|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|9.2|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|9.4|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|10.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|10.5|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|11.2|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|12.4|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other|12.9|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||8.9|16.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|Other||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|4.8|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2013|||||3.7|6.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|5.1|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|5.6|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.0|6.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|5.6|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.0|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.0|San Jose, CA|Percentage of births under 2,500 grams.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.1|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.2|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.4|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.5|San Francisco, CA|Percentage of births under 2,500 grams.||||||5.7|7.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.6|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|6.6|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702016||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.1|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.6|7.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.2|Baltimore, MD|Percentage of births under 2,500 grams.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.2|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||3.5|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.4|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.4|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.3|7.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.4|Phoenix, AZ|Percentage of births under 2,500 grams.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|7.8|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|8.0|San Antonio, TX|Percentage of births under 2,500 grams.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|8.7|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.9|9.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Both|White|10.2|Cleveland, OH|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Female|All|6.9|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2013|Male|All|6.1|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth & Death Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Number of low birth weight out of total live births. (Births w/ unknown birth weight excluded)||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|6.3|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.8|6.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|6.4|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Includes Asian only; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|6.7|San Francisco, CA|Percentage of births under 2,500 grams.||||||6.2|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|7.1|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2014|||||6.4|7.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|7.1|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2015 August 2016.|||||6.5|7.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|7.2|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||7.0|7.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|7.7|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|7.9|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||7.6|8.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|8.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702015||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|8.8|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.5|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|8.9|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|9.1|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|9.4|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|9.4|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|10.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.9|11.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|All|13.8|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|American Indian/Alaska Native|7.3|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|American Indian/Alaska Native|12.8|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|American Indian alone||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|American Indian/Alaska Native||Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT low birth weight query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|6.5|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2014|||||4.8|8.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|6.7|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||5.4|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|7.1|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.5|9.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|7.4|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||6.6|8.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|7.4|San Francisco, CA|Percentage of births under 2,500 grams.||||||6.4|8.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|7.7|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|8.0|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|8.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.5|10.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|8.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702023||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|8.3|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|9.4|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|9.5|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||9.2|9.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|10.1|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|10.6|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.8|12.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Asian/PI|13.8|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|8.8|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||6.9|11.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|10.2|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|10.3|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|10.5|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT low birth weight query|||||8.3|13.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|12.2|San Francisco, CA|Percentage of births under 2,500 grams.||||||8.7|15.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|12.3|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|12.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||11.5|13.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.1|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2014|||||10.9|15.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.2|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||12.1|14.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.2|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702019||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.6|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.6|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||13.2|14.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.7|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.7|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||13.0|14.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.9|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||12.8|15.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|13.9|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|14.6|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|14.9|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Black|15.0|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||13.5|16.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|5.6|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2014|||||4.5|6.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|5.7|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|5.9|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|6.1|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT low birth weight query|||||4.9|7.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|6.3|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|Hispanic Ancestry includes all races||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|6.5|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|6.6|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.1|7.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|6.7|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||5.6|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|6.7|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||6.4|7.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.0|San Francisco, CA|Percentage of births under 2,500 grams.||||||5.7|8.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.1|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||6.9|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702025||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.3|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.3|9.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.4|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.5|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|7.9|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.3|9.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|8.6|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|8.8|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.4|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|8.9|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Hispanic|9.5|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||8.6|10.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Multiracial|7.8|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Multiracial|10.9|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||8.6|13.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Multiracial|16.9|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|6.8|San Francisco, CA|Percentage of births under 2,500 grams.||||||4.5|9.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|8.0|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.8|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|8.0|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||6.8|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|8.2|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|8.4|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||6.7|10.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|8.5|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|8.9|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other|14.3|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||10.4|18.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other||San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|Other||Seattle, WA|Percentage of births under 2,500 grams.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|3.7|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2014|||||2.7|5.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|5.4|San Francisco, CA|Percentage of births under 2,500 grams.||||||4.6|6.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|5.8|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|Oregon Birth Certificates|OPHAT- Low Birth rate query||||5.2|6.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.1|Minneapolis, MN|Percentage of births under 2,500 grams.|Minnesota Department of Health, Vital Records|Multiple births were included in these analyses, consistent with Big Cities methodology. The City of Minneapolis (and Hennepin County) also produce analyses of low birth weight for singleton births only.|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.1|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||5.4|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.4|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.0|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.4|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||6.0|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.7|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.8|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.8|Miami (Miami-Dade County), FL|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|6.9|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||6.4|7.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|7.0|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2015; Table 5: Low birthweight live births, by detailed race and Hispanic origin of mother: United States, selected years 19702017||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|7.1|Boston, MA|Percentage of births under 2,500 grams.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|7.3|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.1|7.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|7.4|Indianapolis (Marion County), IN|Percentage of births under 2,500 grams.|MCPHD Birth/Death Certificate Data|||||6.8|8.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|7.4|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||6.7|8.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|8.3|Denver, CO|Percentage of births under 2,500 grams.|Vital stats|Other category includes other and unknowns|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|8.4|Detroit, MI|Percentage of births under 2,500 grams.|MDHHS|Percent of live births weighing less than 2,500 grams|||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2014|Both|White|8.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.7|9.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|6.2|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2015 August 2016.|||||5.6|6.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|6.4|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||5.8|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|6.5|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|Includes Asian only; No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|7.2|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2015|||||6.5|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|7.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||7.2|7.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.1|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2016, HHS/CDC/NCHS, Table 5 https://www.cdc.gov/nchs/data/hus/2016/005.pdf||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.2|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.3|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||8.0|8.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.6|Boston, MA|Percentage of births under 2,500 grams.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.6|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.6|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||8.5|8.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|8.9|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.5|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|9.0|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||8.4|9.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|9.6|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|10.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.5|10.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|All|10.9|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||10.4|11.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|American Indian/Alaska Native|7.5|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2016, HHS/CDC/NCHS, Table 5 https://www.cdc.gov/nchs/data/hus/2016/005.pdf||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|6.1|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2015|||||4.4|8.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|7.1|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||6.3|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|7.3|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||5.9|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|7.7|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|* indicates data are suppressed due to small numbers||||6.2|9.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|8.1|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|8.2|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||6.5|10.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|8.4|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|8.4|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2016, HHS/CDC/NCHS, Table 5 https://www.cdc.gov/nchs/data/hus/2016/005.pdf||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|8.9|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||8.6|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|9.2|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.6|11.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|9.9|Boston, MA|Percentage of births under 2,500 grams.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|9.9|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Asian/PI|13.0|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||10.7|15.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|8.4|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||6.5|10.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|9.1|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||7.1|11.6
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|10.8|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2015|||||8.8|12.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|11.2|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|11.5|Boston, MA|Percentage of births under 2,500 grams.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|11.7|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.7|12.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|12.2|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||11.2|13.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|13.4|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||12.5|14.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|13.4|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2016, HHS/CDC/NCHS, Table 5 https://www.cdc.gov/nchs/data/hus/2016/005.pdf||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|13.7|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||13.3|14.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|13.8|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|13.8|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||12.3|15.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|14.4|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||12.1|16.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Black|14.5|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|5.1|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.5|7.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|5.8|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2015|||||4.7|7.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|6.0|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|6.1|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|6.6|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.0|8.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|6.9|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||5.6|8.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|7.0|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|7.1|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||6.8|7.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|7.2|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.1|7.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|7.2|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2016, HHS/CDC/NCHS, Table 5 https://www.cdc.gov/nchs/data/hus/2016/005.pdf||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|7.3|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.8|7.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|8.0|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|8.3|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||7.3|9.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|8.8|Boston, MA|Percentage of births under 2,500 grams.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|9.0|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.5|9.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Hispanic|9.8|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||8.9|10.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Multiracial|12.0|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||9.7|14.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other|7.8|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||4.3|11.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other|9.1|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||7.9|10.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other|10.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||9.1|11.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other|14.6|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||11.1|18.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other||Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other||Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to less than 10 observations.|||0.0|0.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other||San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|Other||Seattle, WA|Percentage of births under 2,500 grams.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|5.3|San Diego County, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Birth weight less than 2,500 g (approximately 5lbs, 8oz).|No. based on Race/Ethnicity of infant, while rate denominator is based on the mother's Race/Ethnicity.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|5.4|Seattle, WA|Percentage of births under 2,500 grams.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||4.7|6.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|5.6|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2015|||||4.4|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.0|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.2|Charlotte, NC|Percentage of births under 2,500 grams.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.2|New York City, NY|Percentage of births under 2,500 grams.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.4|Boston, MA|Percentage of births under 2,500 grams.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.5|Los Angeles, CA|Percentage of births under 2,500 grams.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||6.0|6.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.8|Fort Worth (Tarrant County), TX|Percentage of births under 2,500 grams.|Texas Department of State Health Services|||||6.3|7.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|6.9|U.S. Total, U.S. Total|Percentage of births under 2,500 grams.|Health, United States, 2016, HHS/CDC/NCHS, Table 5 https://www.cdc.gov/nchs/data/hus/2016/005.pdf||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|7.4|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||6.7|8.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|7.5|San Antonio, TX|Percentage of births under 2,500 grams.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||6.9|8.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|7.8|Las Vegas (Clark County), NV|Percentage of births under 2,500 grams.|CDC WONDER|||||7.7|8.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|8.1|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||7.3|8.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2015|Both|White|9.1|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.3|10.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|All|6.8|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||6.3|7.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|All|7.4|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2016|||||6.7|8.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|All|9.5|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||8.9|10.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|All|9.8|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|All|10.5|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.9|11.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|All|10.8|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||10.3|11.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Asian/PI|7.7|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||6.4|9.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Asian/PI|7.8|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||6.1|9.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Asian/PI|8.3|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2016|||||6.4|10.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Asian/PI|12.0|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.1|14.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Asian/PI|13.8|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||11.5|16.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Black|9.5|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||7.4|11.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Black|12.0|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2016|||||9.8|14.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Black|12.4|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||11.4|13.5
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Black|13.2|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||11.0|15.4
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Black|14.3|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||13.6|15.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Black|14.8|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Hispanic|5.6|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2016|||||4.5|6.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Hispanic|6.3|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||5.0|7.8
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Hispanic|7.5|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Hispanic|8.3|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.6|10.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Hispanic|9.1|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||8.1|10.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Hispanic|9.3|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||8.4|10.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Multiracial|10.8|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||8.5|13.2
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Other|12.7|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||8.2|17.1
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Other|13.9|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||9.1|18.7
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|Other||Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|White|6.4|Oakland (Alameda County), CA|Percentage of births under 2,500 grams.|Alameda County Vital Statistics Files, 2016|||||5.1|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|White|6.4|Portland (Multnomah County), OR|Percentage of births under 2,500 grams.|OPHAT Low Birth Weight Query|||||5.8|7.0
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|White|6.9|Kansas City, MO|Percentage of births under 2,500 grams.|||||||
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|White|7.2|Philadelphia, PA|Percentage of births under 2,500 grams.|PA Eddie-->Vital Statistics|||||6.6|7.9
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|White|8.5|Denver, CO|Percentage of births under 2,500 grams.|Vital Records|||||7.6|9.3
Maternal and Child Health|Percent of Low Birth Weight Babies Born|2016|Both|White|8.9|Columbus, OH|Percentage of births under 2,500 grams.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.0|9.7
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Both|All|10.4|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Both|Black|17.3|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Both|Hispanic|14.3|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Both|White|4.8|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|2.2|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||1.9|2.6
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|6.5|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|7.1|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|7.2|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|7.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|7.8|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|8.9|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|9.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||8.4|9.5
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|9.3|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|10.2|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|10.6|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|10.9|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|11.3|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|11.4|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|11.8|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||11.1|12.4
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|12.4|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|12.9|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||12.5|13.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|13.1|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||12.6|13.5
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|All|37.3|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2010-2012|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2010-2012|||35.2|39.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|American Indian/Alaska Native|0.0|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|American Indian/Alaska Native|10.0|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|American Indian/Alaska Native|13.6|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|American Indian/Alaska Native|16.1|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 13. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_01.pdf||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|American Indian/Alaska Native|19.5|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|0.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.||Does not include Pacific Islanders as we report data separately for this group |||0.3|1.5
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|0.8|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|0.9|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|1.0|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|1.4|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||0.6|2.2
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|1.6|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|1.8|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|2.5|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||1.6|3.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|2.6|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 13. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|2.8|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|3.4|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||2.5|4.4
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|3.7|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||3.5|3.8
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|3.9|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|4.4|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|6.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||3.5|8.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|8.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Asian/PI|9.5|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2010-2012|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2010-2012|||7.0|12.6
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|6.1|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||4.4|8.2
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|8.9|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|10.6|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|11.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|12.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|12.9|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|13.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||10.9|15.1
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|13.5|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|13.5|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|14.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||13.4|15.7
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|14.7|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|15.0|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|15.2|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||14.6|15.7
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|15.2|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|15.5|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||13.8|17.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|16.7|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|17.2|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||16.4|18.0
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|20.8|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|21.9|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|39.3|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Black|41.5|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2010-2012|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2010-2012|||37.7|45.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|5.7|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|8.2|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||6.1|10.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|10.0|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|10.9|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|11.7|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|12.1|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|12.7|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|13.1|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||12.8|13.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|13.1|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|14.0|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||11.7|16.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|14.1|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|14.3|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|15.1|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||14.0|16.2
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|15.5|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|16.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||15.5|16.6
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|16.3|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|18.0|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|18.6|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||17.2|19.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|41.8|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Hispanic|49.8|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2010-2012|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2010-2012|||45.7|53.8
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Multiracial|6.3|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Multiracial|6.9|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Multiracial|19.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|1.8|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|2.2|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|3.2|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|3.7|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|4.1|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|4.3|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|6.4|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.||Includes multi-race|||3.6|10.5
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|6.9|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother. Other includes Native American/Alaskan, Two or More Races, and Unknown. Data classified as 'Other' in source data was masked due to small numbers (<5) and is not included in the subtotal.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|13.6|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||10.4|16.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|15.5|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other|18.7|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||12.4|24.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|Other||San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|0.6|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|0.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics (CHS), Birth Certificate Data, 1990-2016, Community Health Assessment Tool (CHAT), June 2017.|||||0.4|1.0
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|1.1|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|1.6|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|1.6|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|2.0|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|2.4|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||2.0|2.9
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|2.5|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|2.7|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2010. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|4.5|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|4.7|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||4.2|5.3
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|4.7|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||4.2|5.2
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|5.1|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||5.0|5.2
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|5.3|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|6.3|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|6.3|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|6.7|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|9.3|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.4|10.1
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|9.5|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2010-2012|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2010-2012|||6.7|13.1
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|9.7|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2010|Female|White|15.2|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Both|All|9.4|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Both|Black|15.1|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Both|Hispanic|14.0|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Both|White|4.3|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|2.0|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|5.0|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|5.3|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|6.0|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|6.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|6.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2011. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|6.6|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|6.7|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||6.2|7.1
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|7.6|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||7.1|8.1
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|8.0|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|8.4|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|8.7|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||8.6|8.8
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|9.2|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|9.8|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|10.1|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|10.4|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|10.6|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|11.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||11.3|12.2
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|12.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|12.1|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|12.1|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|All|32.7|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2011-2013|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2011-2013|||30.8|34.7
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|American Indian/Alaska Native|7.3|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|American Indian/Alaska Native|8.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|American Indian/Alaska Native|9.9|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|American Indian/Alaska Native|13.7|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|American Indian/Alaska Native|14.9|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 13. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_01.pdf||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|0.9|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|0.9|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|1.0|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|1.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||0.2|1.7
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|1.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|1.2|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|1.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2011. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|2.3|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|2.3|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|2.3|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 13. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|2.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||0.8|4.2
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|2.5|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|3.2|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||3.0|3.3
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|3.4|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|3.4|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||2.3|4.7
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|3.7|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||2.7|4.7
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|4.3|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|7.6|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2011-2013|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2011-2013|||5.4|10.4
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Asian/PI|10.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|5.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|5.9|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|7.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2011. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|7.8|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|9.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|10.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|11.3|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|11.5|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|11.7|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|11.8|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||9.9|14.0
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|12.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|12.4|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||10.2|14.5
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|12.7|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|12.9|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||11.9|14.0
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|13.4|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||12.9|13.8
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|13.7|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|13.8|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|14.4|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|15.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||15.0|16.5
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|16.1|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|16.2|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|18.8|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|19.1|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|37.8|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2011-2013|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2011-2013|||34.1|41.5
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Black|40.6|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|5.0|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|5.0|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|8.4|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|8.8|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|9.9|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|10.0|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|10.2|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|10.7|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2011. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|11.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|11.2|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|11.6|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.5|13.8
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|11.9|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|12.1|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|12.1|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|12.2|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||12.0|12.5
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|13.0|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||11.4|14.6
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|13.8|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|14.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||12.8|15.1
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|14.2|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|15.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|16.1|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|16.5|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||15.2|17.7
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|18.4|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|41.9|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2011-2013|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2011-2013|||38.2|45.6
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Hispanic|48.4|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Multiracial|3.8|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Multiracial|4.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Multiracial|5.6|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Multiracial|11.0|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Multiracial|13.9|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||10.8|17.5
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Multiracial|17.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|1.6|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|2.3|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|2.9|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|3.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|3.2|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|4.8|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|5.3|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|6.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2011. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother. Other includes Native American/Alaskan, Two or More Races, and Unknown. Data classified as 'Other' in source data was masked due to small numbers (<5) and is not included in the subtotal.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|6.5|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|12.2|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||6.2|18.2
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|16.3|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||12.5|20.2
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|16.9|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other|20.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Native Hawaiian and Other Pacific Islander||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|Other||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|0.5|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|0.5|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|0.9|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|1.2|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|1.3|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|1.4|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|1.6|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|2.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||1.6|2.4
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|2.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|2.4|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|2.4|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2011. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|3.7|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|3.9|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|4.0|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||3.5|4.5
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|4.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|4.6|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||4.1|5.0
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|4.7|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||4.6|4.8
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|4.9|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|6.0|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|6.1|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|6.2|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|6.4|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2011-2013|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2011-2013|||4.1|9.4
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|8.3|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.5|9.1
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|8.5|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2011|Female|White|8.9|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Both|All|8.5|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Both|Black|13.3|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Both|Hispanic|12.5|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Both|White|4.4|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|1.9|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|2.2|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||1.9|2.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|4.6|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|4.7|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|5.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|5.8|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|6.4|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|6.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|6.6|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|7.0|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|7.8|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|8.1|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|8.2|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||8.1|8.3
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|8.5|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|9.1|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|9.4|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.8|10.0
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|9.5|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|IDPH Vital Statistics||Mothers age 15 to 19||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|9.5|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||9.0|10.1
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|9.6|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|10.4|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|10.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||1.4|11.2
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|10.9|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|11.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|All|27.6|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2012-2014|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2012-2014|||25.8|29.4
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|American Indian/Alaska Native|0.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|American Indian/Alaska Native|10.2|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|American Indian/Alaska Native|13.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|American Indian/Alaska Native|14.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|American Indian/Alaska Native|14.2|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 13. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|American Indian/Alaska Native|15.3|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|0.6|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.3|0.9
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|0.7|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|0.7|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|0.8|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|IDPH Vital Statistics||Mothers age 15 to 19||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|0.8|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|0.9|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|1.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|1.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|1.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|1.9|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|2.0|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 13. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|2.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||0.9|4.2
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|2.5|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|2.9|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||2.1|3.8
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|3.0|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||2.9|3.1
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|3.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||2.2|4.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|3.3|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|4.6|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|6.1|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2012-2014|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2012-2014|||4.2|8.7
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI|8.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Asian/PI||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|3.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|5.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|6.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|7.2|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|9.2|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|9.2|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|9.5|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|9.6|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|10.4|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|10.7|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|11.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|11.3|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||9.4|13.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|11.6|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.6|12.6
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|11.7|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|12.0|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|12.3|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||11.9|12.7
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|12.4|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|12.6|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||9.2|16.0
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|12.9|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||11.8|14.0
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|13.2|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|14.1|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|14.2|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|14.4|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||13.7|15.1
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|16.4|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|17.4|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|IDPH Vital Statistics||Mothers age 15 to 19||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|17.6|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|32.1|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2012-2014|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2012-2014|||28.7|35.6
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Black|40.0|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|4.4|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|5.5|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|6.3|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||5.1|7.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|8.0|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|9.0|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|9.2|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|9.7|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|9.8|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||8.6|11.1
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|9.9|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|10.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|10.1|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|10.6|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|10.6|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||9.2|12.2
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|10.9|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|IDPH Vital Statistics||Mothers age 15 to 19||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|11.5|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|11.7|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||11.4|11.9
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|11.9|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|12.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||10.0|14.3
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|12.6|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|12.9|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|13.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|13.1|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|14.9|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|15.1|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|16.1|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||14.8|17.3
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|19.1|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|35.7|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2012-2014|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2012-2014|||32.3|39.2
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Hispanic|49.1|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|2.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|5.2|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|5.7|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|5.9|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|8.9|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|9.6|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||7.0|12.7
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Multiracial|19.3|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|1.5|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|1.5|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|2.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|2.4|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|3.5|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||1.8|5.2
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|3.6|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|3.7|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|4.5|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|5.0|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|6.8|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother. Other includes Native American/Alaskan, Two or More Races, and Unknown. Data classified as 'Other' in source data was masked due to small numbers (<5) and is not included in the subtotal.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|7.9|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|8.8|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||6.9|11.1
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|10.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||7.0|13.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|13.3|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other|13.7|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|Other||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|0.4|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.2|0.6
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|0.5|Washington, DC|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|0.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.1|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.2|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.4|Chicago, Il|Percentage of mothers giving birth under 20 years of age in a given year.|IDPH Vital Statistics||Mothers age 15 to 19||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.5|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.7|Long Beach, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Source: California Department of Public Health Master Statistical File.  Includes data for 2010, 2011, 2012.||Originating dataset does not include data for zip codes 90840, 90822.  Does not include data on infant gender.  Does not include race/ethnicity data.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.9|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|1.9|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2012. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|2.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|2.3|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|2.7|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|4.0|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||3.5|4.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|4.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|4.2|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||4.1|4.3
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|4.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||3.7|4.6
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|4.3|Houston, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Percent in Harris County. Source: Texas Birth Statistics for Harris. Available at http://soupfin.tdh.state.tx.us/birth05.htm.Accessed on May 20, 2015online.|percentage of mother under age 20 by race|Harris County data, not just Houston||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|4.3|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|5.4|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services||Tarrant County (not just Fort Worth)||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|5.6|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2012-2014|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2012-2014|||3.5|8.5
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|5.7|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14. http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|7.4|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||6.7|8.1
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|7.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.8|8.3
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|8.4|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live births, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|8.5|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2012|Female|White|9.8|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Both|Black|11.6|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Both|Hispanic|13.2|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Both|White|3.9|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|1.3|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|1.7|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||1.4|2.0
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|4.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|4.1|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|4.2|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|4.9|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|5.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||4.8|5.6
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|5.2|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|5.6|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|5.8|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|6.3|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|7.0|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|7.3|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||7.2|7.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|8.3|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.8|8.8
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|8.3|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||7.9|8.6
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|9.0|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||8.6|9.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|9.2|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||8.7|9.7
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|9.4|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|10.0|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|10.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|13.9|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|All|24.0|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2013-2015|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2013-2015|||22.3|25.7
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|4.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|6.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|9.1|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|9.3|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|10.2|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||American Indian alone; Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|12.2|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|American Indian/Alaska Native|17.6|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|0.3|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|0.4|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.2|0.6
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|0.6|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|0.8|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|0.8|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|0.9|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||Does not include Pacific Islander||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|1.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|1.1|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|1.7|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|1.9|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|2.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||1.4|3.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|2.5|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|2.5|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||2.4|2.6
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|2.6|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||1.8|3.5
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|3.2|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|3.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||1.6|5.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|5.0|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|5.8|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2013-2015|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2013-2015|||3.9|8.3
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Asian/PI|6.7|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|3.0|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|4.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|5.6|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|5.9|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|6.1|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|6.3|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|6.5|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||4.1|8.9
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|8.6|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|9.0|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|9.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|9.2|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|9.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||7.5|11.2
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|9.6|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.7|10.5
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|9.7|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|10.0|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||9.7|10.3
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|10.5|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||9.6|11.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|10.8|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|11.3|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|11.7|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||10.7|12.8
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|11.7|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||11.0|12.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|12.9|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|14.9|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|15.0|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Black|27.0|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2013-2015|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2013-2015|||23.8|30.2
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|4.0|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|4.4|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|5.8|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||4.6|7.0
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|7.6|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|8.1|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|8.7|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|9.0|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|9.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|9.3|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|9.3|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|9.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|10.5|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|10.6|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||10.4|10.9
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|11.1|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||9.6|12.8
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|11.2|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|11.2|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|11.4|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||9.3|13.5
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|11.4|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||10.1|12.8
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|11.7|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||11.1|12.3
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|12.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|13.1|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|13.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||12.6|14.9
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|13.8|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Hispanic|33.0|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2013-2015|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2013-2015|||29.6|36.3
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|4.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|5.2|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|6.3|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|6.7|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|9.3|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||6.8|12.4
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|12.1|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||10.0|14.2
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Multiracial|17.5|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|0.7|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.1|1.5
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|2.4|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|3.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|3.1|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|4.0|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||3.1|4.8
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|4.3|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|4.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|5.2|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother. Other includes Native American/Alaskan, Two or More Races, and Unknown. Data classified as 'Other' in source data was masked due to small numbers (<5) and is not included in the subtotal.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|7.3|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||5.7|9.2
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|7.7|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||4.6|10.9
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|10.6|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other|15.4|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|Other||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|0.3|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.1|0.5
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|0.3|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Birth Certificate Data, 1990-2013, August 2014.||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|0.7|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|0.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|1.0|San Jose, CA|Percentage of mothers giving birth under 20 years of age in a given year.|Santa Clara County Public Health Department, 2011-2013 Birth Databases||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|1.1|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2010-13||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|1.1|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics|Requested methodology|||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|1.5|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|1.6|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2013. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|2.6|Baltimore, MD|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Statistics Administration birth files for 2011, 2012, 2013, Maryland Department of Health and Mental Hygiene, calculations by Baltimore City Health Dept.||Race/ethnicity is that of the mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|2.7|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|3.5|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||3.1|4.0
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|3.5|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||3.1|4.0
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|3.9|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||3.8|4.0
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|4.0|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|Bexar County resident, SA Metro Health Birth and Death Certificates supplied by Texas DSHS, Preliminary data subject to change.||Bexar County Residents||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|4.1|Phoenix, AZ|Percentage of mothers giving birth under 20 years of age in a given year.|National Vital Statistical Reports Birth: Prelim Data||Mothers race and residential zip code were used to stratify data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|5.1|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||4.7|5.5
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|5.1|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|6.0|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2013-2015|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2013-2015|||3.8|8.9
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|6.9|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||6.2|7.6
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|7.0|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.3|7.8
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|7.6|Cleveland, OH|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2013|Female|White|8.3|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Both|All|6.8|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Both|Black|10.3|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Both|Hispanic|11.3|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Both|White|3.2|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|1.2|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.0|1.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|1.7|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||1.4|2.0
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|3.6|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|3.7|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|4.1|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|4.7|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|5.0|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|5.1|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|5.1|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||4.7|5.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|6.1|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||5.9|6.3
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|6.3|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_12.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|6.5|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||6.4|6.6
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|7.6|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||7.3|7.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|7.7|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.2|8.2
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|7.9|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||7.5|8.2
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|8.1|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||7.6|8.6
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|8.8|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||8.8|9.2
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|11.9|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|All|20.6|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2014-2016|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2014-2016|||19.0|22.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|American Indian/Alaska Native|9.8|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|American Indian/Alaska Native|10.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||American Indian alone||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|American Indian/Alaska Native|11.3|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_12.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|0.4|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|0.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||0.2|0.6
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|0.4|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.2|0.6
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|0.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.3|1.3
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|0.8|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|0.9|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|1.7|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||1.1|2.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|1.7|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_12.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|1.9|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||1.8|2.0
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|2.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|2.3|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||1.5|2.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|2.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||2.0|3.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|3.8|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|4.0|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||2.2|5.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|5.0|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI|5.2|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2014-2016|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2014-2016|||3.4|7.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Asian/PI||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|4.7|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.3|6.4
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|4.9|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|5.2|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|5.6|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|6.8|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|7.3|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||6.6|8.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|7.4|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||4.7|10.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|7.6|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|7.6|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|7.9|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||6.8|9.2
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|8.0|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|9.0|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||8.7|9.3
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|9.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||8.4|10.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|9.4|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||8.6|10.3
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|9.5|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||7.8|11.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|9.5|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_12.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|9.9|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||9.0|10.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|10.3|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||9.7|11.0
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|12.4|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Black|20.9|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2014-2016|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2014-2016|||18.0|23.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|2.8|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.6|4.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|3.5|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|5.2|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||4.1|6.3
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|7.5|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|7.8|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|7.9|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|8.3|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|8.8|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||8.5|9.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|9.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.4|11.0
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|9.4|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||9.2|9.6
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|9.5|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_12.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|10.4|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||9.1|11.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|10.4|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|10.6|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|11.3|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||10.9|11.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|11.5|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||10.8|12.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|11.5|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||10.0|13.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|11.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||9.5|14.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|12.1|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|12.7|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||11.6|13.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Hispanic|30.7|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2014-2016|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2014-2016|||27.6|33.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Multiracial|5.9|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Multiracial|9.8|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|Induced Abortion Records, Oregon Birth Certificates, National Center for Health Statistics Population Estimates (Vintage 2012), Census Bureau Population Estimates (Vintage 2012)|OPHAT- fertility query (add the numerators in age-specific fertility rate / by total births for each year, 95% conf intervals from R0|confidence intervals calculated using clopper-pearson method|||7.3|12.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Multiracial|14.0|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|1.7|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|1.7|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|1.8|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||1.2|2.4
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|3.3|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||1.7|4.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|3.8|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|4.1|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||3.2|4.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|4.6|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother. Other includes Native American/Alaskan, Two or More Races, and Unknown. Data classified as 'Other' in source data was masked due to small numbers (<5) and is not included in the subtotal.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|6.9|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||5.3|8.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|9.4|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||6.2|12.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|11.9|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.9|27.8
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other|12.0|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|Other||San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|0.2|San Francisco, CA|Percentage of mothers giving birth under 20 years of age in a given year.||||||0.0|0.4
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|0.4|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.3|0.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|0.6|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston resident live birth, MA Department of Public Health||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|0.6|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2014|||||0.4|0.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|0.7|Minneapolis, MN|Percentage of mothers giving birth under 20 years of age in a given year.|Minnesota Department of Health, Vital Records||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|1.0|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|1.1|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|1.4|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|1.6|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2014. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|2.1|Miami (Miami-Dade County), FL|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|3.0|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||2.6|3.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|3.5|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||3.4|3.5
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|3.7|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||3.2|4.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|4.0|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||3.7|4.4
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|4.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||3.8|4.7
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|4.6|U.S. Total, U.S. Total|Percentage of mothers giving birth under 20 years of age in a given year.|Table 14 - http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_12.pdf||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|6.1|Oakland (Alameda County), CA|Percentage of mothers giving birth under 20 years of age in a given year.|Alameda County Vital Statistics Files, 2014-2016|Teen Birth Rate (females 15-19 years giving birth) per 1,000 live births among females 15-19 years|Value is for range of years 2014-2016|||4.0|9.1
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|6.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.5|6.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|6.2|Indianapolis (Marion County), IN|Percentage of mothers giving birth under 20 years of age in a given year.|MCPHD Birth/Death Certificate Data|||||5.6|6.9
Maternal and Child Health|Percent of Mothers Under Age 20|2014|Female|White|7.9|Detroit, MI|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records, MDHHS||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Both|All|6.7|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Both|Black|10.7|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Both|Hispanic|9.5|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Both|White|3.4|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|1.2|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||1.0|1.5
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|3.2|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|3.3|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|4.0|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|4.8|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|4.8|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||4.5|5.2
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|5.4|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||5.2|5.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|6.7|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||6.4|7.0
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|7.1|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.6|7.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|7.2|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||6.9|7.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|All|7.8|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||7.5|8.1
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|0.2|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||0.0|0.3
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|0.5|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.2|1.0
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|0.6|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|0.7|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|1.2|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||0.6|2.1
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|1.5|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||1.5|1.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|1.5|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||0.9|2.1
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|2.4|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|2.4|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||1.5|3.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Asian/PI|2.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||1.1|3.9
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|4.0|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||2.7|5.7
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|4.4|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|4.8|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|6.8|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||6.0|7.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|7.0|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|7.6|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||6.5|8.9
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|7.7|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||7.0|8.5
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|8.1|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.3|9.0
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|8.9|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||8.7|9.2
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|9.2|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||8.6|9.8
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|9.5|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||7.8|11.4
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Black|34.7|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|4.8|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||3.2|6.9
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|6.7|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|7.0|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|7.9|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||7.6|8.2
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|8.1|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|8.4|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.6|10.1
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|8.9|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||8.7|9.0
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|9.0|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||7.7|10.4
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|9.9|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||9.3|10.5
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|9.9|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||9.4|10.3
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|12.4|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||11.3|13.5
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Hispanic|54.8|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Multiracial|10.0|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||7.9|12.1
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other|1.3|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||0.8|1.8
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other|3.3|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other|3.9|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||3.1|4.7
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other|4.4|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother. Other includes Native American/Alaskan, Two or More Races, and Unknown. Data classified as 'Other' in source data was masked due to small numbers (<5) and is not included in the subtotal.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other|7.4|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||4.8|10.0
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other|13.8|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||8.1|21.4
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other||Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to <20 events.||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|Other||San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|0.4|Seattle, WA|Percentage of mothers giving birth under 20 years of age in a given year.|Washington State Department of Health, Center for Health Statistics, Death Certificate Data, 1990-2015, August 2016.|||||0.3|0.7
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|0.6|Los Angeles, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics Section, 2015|||||0.4|0.7
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|1.1|New York City, NY|Percentage of mothers giving birth under 20 years of age in a given year.|NYC DOHMH Bureau of Vital Statistics||Race/ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|1.2|Charlotte, NC|Percentage of mothers giving birth under 20 years of age in a given year.|NC DHHS/SCHS/MCPH Epidemiology||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|1.3|San Diego County, CA|Percentage of mothers giving birth under 20 years of age in a given year.|California Department of Public Health, Health Information and Research Center, Birth Statistical Master File, 2015. County of San Diego, Health & Human Services Agency, Public Health Services, Maternal, Child, and Family Heath Services.|Proportion of births to mothers under age 20 out of all ages live births|Percent based on Race/Ethnicity of mother||||
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|2.8|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||2.4|3.2
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|3.4|San Antonio, TX|Percentage of mothers giving birth under 20 years of age in a given year.|CDC, National Center for Health Statistics. Underlying Cause of Death 1999-2016 on CDC Wonder Online Database, released Dec 2017||Bexar County level data|||3.0|3.8
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|3.5|Las Vegas (Clark County), NV|Percentage of mothers giving birth under 20 years of age in a given year.|CDC WONDER|Percent of births to teen mothers||||3.5|3.6
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|3.9|Fort Worth (Tarrant County), TX|Percentage of mothers giving birth under 20 years of age in a given year.|Texas Department of State Health Services|||||3.6|4.3
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|4.1|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||3.7|4.5
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|6.4|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||5.7|7.1
Maternal and Child Health|Percent of Mothers Under Age 20|2015|Female|White|7.3|Boston, MA|Percentage of mothers giving birth under 20 years of age in a given year.|Boston Resident Live Births, Massachusetts Department of Public Health (data as of August 2, 2016)||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Both|All|5.7|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Both|Black|8.6|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Both|Hispanic|10.1|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Both|White|2.7|Kansas City, MO|Percentage of mothers giving birth under 20 years of age in a given year.|||||||
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|All|4.0|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||3.7|4.4
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|All|6.1|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||5.7|6.4
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|All|6.9|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||6.4|7.4
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Asian/PI|1.2|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||0.1|2.2
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Asian/PI|2.1|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||1.4|2.8
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Asian/PI|2.2|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||1.4|3.4
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Asian/PI|4.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||2.4|6.0
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Black|6.3|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||4.9|8.0
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Black|6.4|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||4.8|7.9
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Black|8.0|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.2|8.8
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Black|8.1|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||7.5|8.7
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Hispanic|8.6|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||7.3|10.0
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Hispanic|9.3|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||8.3|10.3
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Hispanic|9.7|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||7.9|11.5
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Hispanic|10.2|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||9.2|11.3
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Multiracial|8.9|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||6.8|11.1
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Other|2.8|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||0.6|5.0
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Other|10.5|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health||Includes Unknown Race|||6.2|14.7
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|Other||Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Here, the data is suppressed due to a population less than 100 individuals.|||0.0|0.0
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|White|1.4|Denver, CO|Percentage of mothers giving birth under 20 years of age in a given year.|Vital Records||Mothers between 15-19 years of age|||1.1|1.8
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|White|2.3|Philadelphia, PA|Percentage of mothers giving birth under 20 years of age in a given year.|PA Eddie-->Vital Statistics|||||1.9|2.6
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|White|3.5|Portland (Multnomah County), OR|Percentage of mothers giving birth under 20 years of age in a given year.|OPHAT Mom &lt;20 query|||||3.1|3.9
Maternal and Child Health|Percent of Mothers Under Age 20|2016|Female|White|5.2|Columbus, OH|Percentage of mothers giving birth under 20 years of age in a given year.|Ohio Department of Health Vital Statistics Data Analyzed by Columbus Public Health|||||4.5|5.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|209.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||200.1|218.5||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|346.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|419.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||411.9|427.2|410.5|428.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|432.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|495.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|501.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||489.6|514.1|487.3|516.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|549.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||543.0|555.0|542.0|557.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|633.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|656.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|646.78|667.09|669.1|644.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|687.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||668.6|706.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|770.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|925.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|964.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|981.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|1063.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|All|1273.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|60.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|97.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|120.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||84.8|156.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|123.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|150.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||141.0|159.0|139.0|161.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|180.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|202.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|232.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|259.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI|263.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|932.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|943.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|988.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1085.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1196.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1153.3|1238.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1271.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1235.0|1309.5|1228.1|1316.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1483.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1576.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1611.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||1579.0|1643.0|1572.0|1649.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|1627.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|2320.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black|2562.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|166.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|186.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||162.6|212.6|158.4|217.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|299.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|312.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|356.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||318.5|394.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|466.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||458.0|474.0|457.0|475.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|474.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|572.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|715.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|702.0|729.66|732.3|699.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|902.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic|912.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other|102.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||54.9|149.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other|552.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|498.14|606.12|616.8|487.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other|610.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other|1001.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other|3717.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other|4176.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|50.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|138.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|143.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|147.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||134.1|160.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|160.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|161.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|211.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||204.0|218.0|203.0|219.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|215.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|220.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||210.1|231.1|208.2|233.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|232.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|273.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White|383.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|369.47|397.65|400.4|366.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|294.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||279.2|310.2||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|471.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|605.4|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||592.4|618.4|589.9|620.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|609.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|655.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||635.9|674.9|632.2|678.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|682.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|694.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||684.0|703.0|682.0|705.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|763.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|922.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|905.17|938.88|942.1|901.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|960.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||928.8|991.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|992.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|1191.5|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|1312.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|1354.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|1471.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Female|All|1582.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|118.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||108.4|127.9||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|213.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|236.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||227.9|244.1|226.4|245.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|251.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|308.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|337.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||322.7|351.8|320.0|354.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|381.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|396.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||375.6|417.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|400.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||392.0|407.0|391.0|408.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|489.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|524.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|574.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|624.2|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|630.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|635.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2010|Male|All|927.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|266.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||256.3|277.1||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|349.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|451.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||444.0|459.8|442.5|461.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|474.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|491.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|555.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||549.0|561.0|548.0|563.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|658.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|670.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|672.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|672.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|662.56|682.93|684.9|660.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|784.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|933.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|945.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||923.4|968.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|977.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|989.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||972.5|1006.7|969.3|1010.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|1156.0|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|All|1341.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|69.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|103.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|104.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|105.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|152.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|158.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||117.8|199.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|171.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||161.0|181.0|159.0|183.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|212.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|223.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI|367.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|863.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|867.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|878.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1121.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1146.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1172.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1451.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||1421.0|1482.0|1415.0|1488.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1585.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1536.5|1634.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1620.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|1820.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|2223.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black|2492.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2442.0|2544.7|2432.4|2554.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|146.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|330.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|373.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||339.9|409.5|333.8|416.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|392.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|483.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||475.0|492.0|474.0|493.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|484.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||440.8|529.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|541.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|597.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|697.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|723.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic|1023.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other|385.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||294.0|477.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other|605.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|549.98|660.62|671.6|539.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other|702.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other|1175.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other|4039.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other|4338.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|55.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|120.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|122.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|154.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|161.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|166.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|185.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||170.7|200.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|221.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||214.0|228.0|213.0|229.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|240.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|321.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|432.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||418.0|447.3|415.3|450.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White|485.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|469.53|501.1|504.1|466.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|376.4|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||358.8|393.9||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|476.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|628.8|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||615.6|642.1|613.1|644.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|663.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|669.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|698.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||688.0|707.0|686.0|709.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|793.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|871.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|932.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|915.64|949.24|952.5|912.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1005.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1253.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1290.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1263.4|1317.7|1258.3|1322.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1309.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1272.8|1345.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1311.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1445.3|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Female|All|1687.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|147.2|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||136.3|158.1||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|214.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|277.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||268.4|285.9|266.7|287.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|281.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|313.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|403.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|391.84|414.34|416.5|389.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|409.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||401.0|416.0|400.0|417.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|433.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|534.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|535.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|548.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||524.4|573.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|556.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|621.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|665.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||645.9|686.5|642.1|690.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|831.4|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2011|Male|All|953.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|291.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||280.2|301.9||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|376.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|439.8|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||432.0|447.6|430.5|449.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|450.2|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||435.1|465.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|500.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|524.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|530.8|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|560.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|573.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||567.0|579.0|565.0|580.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|633.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|623.8|643.38|645.3|621.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|716.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|746.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|790.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||770.1|810.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|944.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|954.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|1040.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1023.5|1058.4|1020.2|1061.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|1239.3|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|All|1363.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|American Indian/Alaska Native|7028.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||6111.2|8039.3|5947.8|8235.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|81.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|90.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|104.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|111.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|117.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||82.5|152.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|133.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|176.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||165.0|186.0|164.0|188.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|191.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||156.9|230.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|213.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|265.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI|401.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|661.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|668.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|811.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|844.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|886.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|1036.2|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||941.0|1131.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|1301.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|1348.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||1319.0|1378.0|1313.0|1383.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|1365.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1319.4|1410.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|1656.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|1692.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|2152.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black|2493.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2443.3|2545.1|2433.7|2555.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|102.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|364.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|372.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|387.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||344.8|429.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|414.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||374.1|455.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|500.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||492.0|508.0|490.0|510.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|510.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||471.9|552.2|464.7|560.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|591.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|654.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|661.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|648.15|674.18|676.7|645.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|785.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic|1071.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|221.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||143.4|327.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|408.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||314.0|502.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|557.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|505.62|609.65|619.9|495.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|794.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|1043.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|2750.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other|3826.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|68.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|94.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|126.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|143.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|165.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|173.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||158.8|187.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|180.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|203.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||191.8|214.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|252.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||244.0|259.0|243.0|261.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|325.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|364.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|466.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||451.3|481.7|448.5|484.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White|547.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|408.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||390.4|427.0||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|513.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|564.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||540.5|588.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|612.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||599.6|625.7|597.1|628.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|700.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||690.0|709.0|688.0|711.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|700.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|705.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|722.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|744.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|857.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|895.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|878.67|911.31|914.4|875.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|944.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|1109.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1075.4|1142.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|1254.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|1284.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|1384.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1356.8|1412.7|1351.5|1418.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|1496.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|All|1699.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|Asian/PI|316.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|Black|1111.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|Hispanic|510.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Female|White|179.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|167.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||155.9|179.2||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|232.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|268.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||260.3|277.5|258.6|279.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|297.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|329.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|333.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||315.0|351.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|344.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|363.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|352.49|373.63|375.7|350.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|367.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|442.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||434.0|449.0|433.0|451.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|450.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||427.9|472.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|527.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|562.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|604.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|635.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|671.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||651.2|691.8|647.4|695.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|838.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|All|987.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|Asian/PI|101.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|Black|647.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|Hispanic|206.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2012|Male|White|120.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|335.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||324.2|347.5||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|399.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|477.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||469.1|485.4|467.6|487.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|479.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|497.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|502.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|513.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||497.8|529.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|605.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||598.0|611.0|597.0|612.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|633.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|623.76|643.16|645.0|621.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|688.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|689.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|772.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||751.9|792.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|803.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|890.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|964.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|1034.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1016.8|1051.4|1013.5|1054.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|1187.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|All|1282.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|96.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|104.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|114.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|128.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||91.8|165.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|152.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|156.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|194.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||183.0|205.0|181.0|207.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|196.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||162.1|235.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|209.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|281.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI|333.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|691.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|694.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|699.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1036.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||941.9|1131.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1044.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|997.92|1091.21|1100.1|989.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1223.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1253.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1315.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||1286.0|1344.0|181.0|1350.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1348.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1303.4|1394.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1503.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1570.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|1944.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black|2449.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2399.7|2499.8|2390.3|2509.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|127.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|345.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|364.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|475.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||429.3|522.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|522.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|528.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||519.0|536.0|518.0|538.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|531.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||485.7|578.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|589.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||547.7|632.9|540.1|641.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|597.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|678.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|664.97|691.05|693.6|662.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|845.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic|962.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|291.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||200.9|409.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|615.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|914.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|849.23|979.53|992.4|836.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|1037.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||887.5|1188.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|1851.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|3044.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other|4424.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|69.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|116.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|138.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|162.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|165.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||151.5|179.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|171.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|226.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|235.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||223.6|247.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|262.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||254.0|270.0|253.0|271.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|301.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|348.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|435.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|420.72|450.36|453.2|418.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White|478.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||463.6|494.4|460.8|497.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|460.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||441.2|480.0||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|522.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|641.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|649.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||623.7|674.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|663.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|680.2|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||666.5|694.0|663.8|696.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|691.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|729.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||719.0|738.0|717.0|740.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|863.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|885.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|869.04|901.21|904.3|866.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|915.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|955.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|1071.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1038.4|1104.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|1158.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|1289.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|1352.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1325.6|1380.6|1320.4|1385.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|1408.2|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|All|1578.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|Asian/PI|290.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|Black|792.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|Hispanic|423.8|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Female|White|155.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|212.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||199.0|225.2||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|267.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|276.5|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||267.8|285.3|266.1|286.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|298.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|307.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|339.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|373.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|374.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||355.2|394.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|450.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|452.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||429.9|474.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|477.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||469.0|485.0|467.0|486.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|495.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|608.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|638.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|639.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|690.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||670.5|711.4|666.7|715.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|929.2|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|All|950.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|Asian/PI|80.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|Black|469.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|Hispanic|200.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2013|Male|White|118.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|365.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||353.0|377.3||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|404.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|483.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|513.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|519.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||511.4|528.4|509.8|530.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|520.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|538.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||522.1|554.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|598.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|589.24|607.91|609.7|587.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|661.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||655.0|668.0|653.0|669.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|699.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|702.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|807.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|885.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|889.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||868.0|911.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|1008.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||991.1|1025.2|987.9|1028.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|1025.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|1048.0|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|All|1240.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|56.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|86.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|89.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|131.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||94.2|168.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|142.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|174.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|179.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||147.3|216.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|209.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|212.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|221.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||209.0|232.0|207.0|234.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|237.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||190.3|293.1|182.2|304.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI|323.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|322.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|785.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|798.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||716.1|880.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|892.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|939.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1174.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1329.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1348.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||1318.0|1378.0|1313.0|1383.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1481.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1486.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1531.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1483.4|1580.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|1790.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black|2271.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2223.9|2319.7|2214.9|2329.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|136.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|249.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|392.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||350.3|433.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|409.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|558.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|570.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|587.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||578.0|596.0|576.0|598.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|593.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|581.84|605.97|608.3|579.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|609.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||559.8|658.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|687.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||642.9|733.9|634.7|742.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|871.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic|990.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|401.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||294.2|536.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|560.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|800.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|884.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||745.9|1023.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|1988.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|1894.79|2082.65|2101.2|1876.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|3750.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other|5563.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|71.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|100.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|152.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|167.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|222.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|226.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||214.9|238.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|229.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|234.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||218.0|251.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|333.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|341.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||332.0|349.0|330.0|351.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|400.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|386.38|414.68|417.4|383.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|407.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White|494.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||479.2|510.5|476.3|513.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|486.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||466.8|506.7||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|523.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|630.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|634.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|652.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||627.4|678.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|706.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|730.5|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||716.2|744.8|713.5|747.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|761.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||751.0|771.0|749.0|773.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|833.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|818.05|848.97|851.9|815.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|849.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|895.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|954.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|1139.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|1195.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1160.4|1230.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|1283.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|1328.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|1346.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1318.9|1373.5|1313.7|1378.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|All|1506.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|Asian/PI|260.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|Black|727.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|Hispanic|376.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Female|White|149.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|243.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||229.6|257.7||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|278.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|309.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||300.5|319.0|298.7|320.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|331.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|336.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|355.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|356.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|345.69|366.21|368.2|343.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|383.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|421.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||401.2|442.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|499.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|532.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|557.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||548.0|565.0|547.0|567.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|562.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||538.2|587.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|615.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|643.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||624.1|663.6|620.5|667.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|646.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|722.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|754.3|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|All|943.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|Asian/PI|113.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|Black|485.8|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|Hispanic|141.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2014|Male|White|124.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|425.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||412.5|438.8||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|445.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|516.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||508.4|525.4|506.8|527.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|533.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|535.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|592.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||575.3|609.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|647.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|672.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||665.0|679.0|664.0|680.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|689.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|703.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|736.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|761.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|785.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|903.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|941.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||924.8|957.7|921.7|960.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|1008.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||985.0|1031.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|1086.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|1256.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|All|1402.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|33.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|77.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|100.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|106.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|106.2|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|164.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|188.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||144.2|233.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|210.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||167.9|261.4|160.6|271.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|211.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||176.8|250.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|229.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|242.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||230.0|254.0|228.0|256.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|281.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI|411.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|249.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|800.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|843.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|946.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|946.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|903.63|990.06|998.3|895.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|976.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1023.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||931.2|1116.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1205.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||1177.0|1232.0|1171.0|1238.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1227.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1449.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1474.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1632.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1582.2|1682.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1706.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|1787.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black|2054.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2009.5|2100.1|2001.0|2108.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|88.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|268.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|333.9|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|402.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|445.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||401.4|489.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|535.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|546.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||538.0|555.0|536.0|557.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|579.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|620.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||578.4|663.9|570.7|672.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|723.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|769.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||713.6|824.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|1083.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic|1104.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|225.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|194.27|256.25|262.4|188.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|313.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|592.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||460.7|749.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|874.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|879.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||740.7|1017.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|4169.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other|5493.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|62.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|138.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|180.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|189.3|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|227.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|242.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|264.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|271.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||254.1|289.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|288.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||275.7|302.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|337.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||329.0|346.0|327.0|348.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|348.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|422.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|491.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||476.4|507.7|473.5|510.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White|634.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|549.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||527.9|570.2||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|567.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|688.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|697.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||671.4|723.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|708.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|713.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||699.1|727.4|696.4|730.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|759.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||749.0|769.0|747.0|771.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|856.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|868.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|898.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|913.2|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|933.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1012.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1174.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1204.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1178.6|1230.3|1173.7|1235.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1352.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1315.6|1389.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1373.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1506.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|All|1553.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|American Indian/Alaska Native|964.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|Asian/PI|284.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|Black|990.8|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|Hispanic|494.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Female|White|250.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|302.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||287.3|318.6||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|314.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|320.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||311.3|330.1|309.5|331.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|360.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|372.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|378.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|438.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|426.78|449.31|451.5|424.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|484.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||462.5|506.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|496.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|547.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|580.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||572.0|589.0|570.0|591.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|601.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|621.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|639.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||613.2|666.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|645.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|658.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||638.3|678.1|634.6|682.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|798.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|974.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|All|1197.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|Asian/PI|86.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|Black|549.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|Hispanic|214.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2015|Male|White|148.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|467.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|471.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||457.8|485.4||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|499.7|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|574.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|580.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|589.8|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||580.7|598.8|579.0|600.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|631.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||614.4|649.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|640.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|651.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||644.0|657.0|643.0|659.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|690.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|680.68|700.37|702.3|678.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|801.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|812.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|860.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|979.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|1032.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1015.5|1049.9|1012.3|1053.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|1091.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|1310.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|All|1424.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|37.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|118.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|145.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|150.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||141.0|160.0|139.0|161.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|159.8|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|173.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|215.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|231.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||195.8|271.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|239.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||195.5|291.5|187.9|301.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|247.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI|431.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|265.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|862.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|863.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|822.51|903.92|911.7|814.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|966.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||877.5|1055.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|970.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||945.0|995.0|940.0|1000.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|981.2|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|1030.2|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|1171.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|1349.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|1474.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|1859.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|1932.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black|2193.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2147.1|2240.2|2138.4|2249.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|122.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|277.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|334.0|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|391.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||384.0|399.0|383.0|400.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|420.1|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|439.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||396.2|482.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|577.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|639.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|685.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|673.23|698.57|701.0|670.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|760.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||714.8|808.2|706.3|817.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|843.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic|1157.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other|109.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|88.01|130.34|134.5|83.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other|649.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||511.7|812.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other|696.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other|4691.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other|6468.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.Gender specific age groups and race/ethnicity calculatons exclude |Not Specified from the denominator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|63.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|179.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|188.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|237.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||229.0|244.0|228.0|245.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|237.6|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|239.2|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|269.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|280.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|336.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||322.7|351.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|363.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|395.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|548.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||532.2|565.4|529.1|568.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White|752.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|733.4|772.08|775.8|729.7
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Boston Public Health Commission has suppressed this value because over 20% of data was unavailable.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|573.4|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|577.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|602.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||579.8|624.2||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|713.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|726.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||716.0|736.0|714.0|737.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|733.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||707.1|759.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|759.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|805.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||790.0|820.0|787.1|822.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|873.6|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|935.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|919.55|951.73|954.8|916.5
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|950.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1012.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1022.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1246.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1324.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1297.3|1351.3|1292.2|1356.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1348.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1469.9|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|All|1542.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|American Indian/Alaska Native|1086.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|Asian/PI|365.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|Black|936.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|Hispanic|543.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Female|White|231.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|342.5|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||325.8|359.1||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|350.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|375.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||365.5|385.9|363.6|387.8
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|397.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|400.9|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|422.1|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|434.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|438.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|427.34|449.68|451.8|425.2
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|527.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Chlamydia query|||||504.7|549.9
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|569.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||560.0|578.0|559.0|579.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|571.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|591.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|699.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|718.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||698.4|739.9|694.6|744.0
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|760.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|835.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|1045.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|All|1306.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|American Indian/Alaska Native|1342.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|Asian/PI|134.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|Black|727.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|Hispanic|221.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2016|Male|White|181.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|All|635.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|All|1082.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1064.6|1099.7|1061.3|1103.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Asian/PI|168.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Asian/PI|241.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||197.1|293.2|189.5|303.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Black|1398.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Black|2344.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2297.4|2393.3|2288.3|2402.6
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Hispanic|576.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Hispanic|868.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||819.7|919.2|810.6|929.1
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|Other|8087.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|White|181.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Both|White|539.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||523.4|556.2|520.4|559.4
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Female|All|815.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Female|All|1363.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1336.2|1391.0|1331.1|1396.3
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Male|All|448.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Chlamydia Rate (Per 100,000 People)|2017|Male|All|779.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||758.0|801.1|754.0|805.3
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|4.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2010 LAC vital statistics data||1.0|8.0|1.0|8.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|4.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 live births.|Division of Disease Control, Philadelphia Dept of Public Health||Data broken down by gender or race/ethnicity not available||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|8.7|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|10.9|Washington, DC|Incidence reported as a crude rate per 100,000 live births.|District of Columbia Public Health Information System (STD Surveillance Data); District of Columbia Vital Records (Natality Data)||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|15.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|20.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|22.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|38.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All|39.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|18.98|60.12|64.2|14.9
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|All||San Jose, CA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is for a range of years 2010-2016.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI|6.1|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2010 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is for a range of years 2010-2016.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black|19.7|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2010 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is for a range of years 2010-2016.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic|5.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic|7.9|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic|31.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic|51.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|23.17|79.43|85.0|17.6
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic|52.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2010 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is for a range of years 2010-2016.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2010 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is for a range of years 2010-2016.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White|3.1|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White|12.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2010 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Value is for a range of years 2010-2016.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Female|All|8.9|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Male|All|8.5|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2010|Male|All|26.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|7.9|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|10.8|Washington, DC|Incidence reported as a crude rate per 100,000 live births.|District of Columbia Public Health Information System (STD Surveillance Data); District of Columbia Vital Records (Natality Data)||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|14.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|15.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|15.49|53.1|56.8|11.8
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|16.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2011 LAC vital statistics data||7.0|25.0|6.0|27.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|17.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 live births.|Division of Disease Control, Philadelphia Dept of Public Health||Data broken down by gender or race/ethnicity not available||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|25.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All|34.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI|5.6|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2011 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black|12.2|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2011 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic|11.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2011 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Hispanic||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other|5.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2011 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White|0.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2011 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Female|All|9.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Male|All|6.9|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2011|Male|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|3.5|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|9.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2012 LAC vital statistics data||2.0|16.0|1.0|17.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|16.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|18.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|22.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 live births.|Division of Disease Control, Philadelphia Dept of Public Health||Data broken down by gender or race/ethnicity not available||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|39.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|39.32|91.52|96.7|34.2
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All|64.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI|0.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2012 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black|12.7|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2012 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic|2.8|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic|80.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|45.27|116.29|123.3|38.3
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic|81.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2012 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Hispanic||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2012 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White|0.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2012 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Female|All|1.8|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Male|All|5.2|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2012|Male|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|4.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 live births.|Division of Disease Control, Philadelphia Dept of Public Health||Data broken down by gender or race/ethnicity not available||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|5.4|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|8.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2013 LAC vital statistics data||1.0|14.0|0.0|15.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|10.8|Washington, DC|Incidence reported as a crude rate per 100,000 live births.|District of Columbia Public Health Information System (STD Surveillance Data); District of Columbia Vital Records (Natality Data)||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|28.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All|63.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI|5.5|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2013 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black|4.4|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black|5.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2013 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic|5.8|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic|78.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|44.11|113.33|120.2|37.3
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic|80.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2013 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Hispanic||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2013 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White|0.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2013 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Female|All|11.1|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Male|All|0.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2013|Male|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|11.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|18.1|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|18.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 live births.|Division of Disease Control, Philadelphia Dept of Public Health||Data broken down by gender or race/ethnicity not available||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|18.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|20.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|20.07|59.57|63.5|16.2
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|21.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2014 LAC vital statistics data||11.0|31.0|9.0|33.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|39.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All|43.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI|5.5|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2014 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black|48.1|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2014 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic|8.8|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic|23.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2014 LAC vital statistics data|Rates based on observations fewer than 12 may not be reliable.|9.0|37.0|6.0|39.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic|49.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|22.13|75.86|81.2|16.8
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic|50.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Hispanic||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2014 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White|5.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White|5.9|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2014 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Female|All|16.7|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Male|All|19.4|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2014|Male|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|8.1|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|17.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|17.05|54.02|57.7|13.4
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|18.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 live births.|Division of Disease Control, Philadelphia Dept of Public Health||Data broken down by gender or race/ethnicity not available||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|18.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|20.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2015 LAC vital statistics data||9.0|30.0|7.0|32.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|22.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|24.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All|35.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI|0.0|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI|0.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2015 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black|26.2|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2015 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic|2.9|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic|12.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic|33.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic|36.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|13.91|59.65|64.2|9.4
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic|38.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2015 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2015 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White|5.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2015 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population:Texas DSHS, http://healthdata.dshs.texas.gov/VitalStatistics/Birth, accessed 11/15/2017|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Female|All|7.4|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Female|All|23.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Male|All|8.8|New York City, NY|Incidence reported as a crude rate per 100,000 live births.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is the annual number of live births in New York City, per the NYC DOHMH Bureau of Vital Statistics, using the most recent completed year as the denominator.|||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2015|Male|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|10.1|Washington, DC|Incidence reported as a crude rate per 100,000 live births.|District of Columbia Public Health Information System (STD Surveillance Data); District of Columbia Vital Records (Natality Data)||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|20.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|24.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|24.9|66.68|70.8|20.8
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|25.5|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 live births.|California Department of Public Health, STD Control Branch||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Cases of congetical syphillis were not broken down by race ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|30.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2016 LAC vital statistics data||17.0|43.0|15.0|45.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|33.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|Live births data is from National Center for Health Statistics.  Census does not have live birth data|Incidence reported as a crude rate per 100,000 live births, using 2010 NCHS natality data.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. |15.2|52.1|11.7|55.6
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|60.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All|67.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2016 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Asian/PI||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black|120.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2016 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Black||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic|14.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic|28.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2016 LAC vital statistics data|Rates based on observations fewer than 12 may not be reliable.|12.0|44.0|9.0|47.0
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic|52.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Data includes rates for Bexar County and San Antonio.|25.02|79.28|84.6|19.7
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic|193.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Hispanic||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2016 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 live births.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority. These figures have been confirmed with the Colorado Department of Public Health and Environment and no cases were reported during this time period. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 live births.|||||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White|1.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed the values between the years 2010-2016 due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||Los Angeles, CA|Incidence reported as a crude rate per 100,000 live births.|STD Surveillance Database; California Department of Public Health, Center for Health Statistics, OHIR Vital Statistics, 2010-2016|Incidence reported as a crude rate per 100,000 live births using 2016 LAC vital statistics data| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||San Antonio, TX|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Both|White||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2016|Male|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 live births.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; CDC Wonder Natality data set, United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention. (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2015, on CDC WONDER Online Database, February 2017. Accessed at http://wonder.cdc.gov/natality-current.html on Jan 12, 2018 6:25:03 PM.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 live births using the appropriate population stratifications and year population estimates as pulled from CDC Wonder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|All|25.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|Asian/PI|0.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|Black|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|Black||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|Hispanic|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|Other|7.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|Other||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|White|11.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 live births.|Prism|Incidence reported as a crude rate per 100,000 births, using ADHS 2010 reported total birth count value for Maricopa County as 54236.|Arizona State Health Department 2010 reported number of births chosen over the US 2010 Census 'persons under 5 years'.||||
Sexually Transmitted Infections|Congenital Syphilis Rate (Per 100,000 Live Births)|2017|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 live births.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|24.2|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||21.1|27.3||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|63.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|65.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|90.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||86.5|93.5|85.8|94.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|97.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|111.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|146.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||142.0|149.0|142.0|149.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|149.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|197.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|202.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||195.0|210.7|193.6|212.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|202.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|197.03|208.31|209.4|196.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|228.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||217.1|239.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|347.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|421.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|428.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|All|441.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea .  Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|6.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|9.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|10.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||0.2|21.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|25.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|28.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|35.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||30.0|39.0|29.0|40.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|42.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|53.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI|58.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|169.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|250.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|305.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|380.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|477.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||450.0|503.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|569.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||550.0|588.0|546.0|593.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|579.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||554.6|605.1|549.9|610.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|645.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|765.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|723.74|806.79|814.7|715.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|828.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea .  Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|893.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black|1194.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|25.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||15.1|35.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|31.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|32.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|49.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|67.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|75.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||72.0|78.0|71.0|79.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|78.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|176.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|190.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic|196.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.|31.2|55.7|29.4|58.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other|68.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||29.6|106.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other|86.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other|177.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|146.92|208.16|214.2|140.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other|341.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other|443.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other|742.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|22.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|22.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|27.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|32.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||26.3|38.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|38.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|61.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|63.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|64.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||59.2|70.7|58.2|71.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|68.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|84.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea .  Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|93.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|86.36|100.26|101.6|85.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White|124.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||119.0|130.0|119.0|131.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|20.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|21.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||17.5|25.9||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|34.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|56.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|79.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|80.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|84.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||79.9|89.6|78.9|90.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|97.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||93.0|100.0|92.0|101.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|125.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|157.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|199.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|191.98|207.68|209.2|190.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|201.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||190.8|212.5|188.8|214.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|221.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||206.2|236.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|337.6|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|408.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|449.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Female|All|493.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea .  Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|26.5|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||21.9|31.1||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|70.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|95.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||90.0|100.2|89.0|101.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|95.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|116.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|120.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|146.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|176.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|191.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|193.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||188.0|198.0|187.0|199.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|204.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||193.0|215.7|191.0|218.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|205.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|235.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||219.1|251.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|358.5|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|387.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea .  Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|393.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2010|Male|All|450.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|30.8|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||27.2|34.3||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|69.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|91.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|93.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|94.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||90.9|98.2|90.2|98.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|102.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|110.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|137.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|143.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||140.0|146.0|139.0|147.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|173.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|196.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|190.98|201.99|203.0|189.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|276.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|289.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||276.8|301.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|303.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||293.6|312.7|291.8|314.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|376.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|443.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|All|445.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|9.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|13.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||1.7|25.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|20.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|22.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|29.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|33.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|42.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||37.0|47.0|36.0|48.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI|47.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|172.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|273.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|283.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|449.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|452.4|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|500.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|467.29|533.68|540.0|460.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|512.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|541.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||523.0|560.0|519.0|564.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|589.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|600.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||570.5|631.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|675.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|858.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||828.2|889.1|822.6|895.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black|1045.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|33.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|35.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||23.6|47.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|35.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|55.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|61.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||48.7|77.8|46.4|80.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|65.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|69.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|83.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||80.0|87.0|79.0|87.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|91.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|135.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|148.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic|208.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|16.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|79.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||37.8|121.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|92.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|155.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|130.46|187.13|192.7|124.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|332.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|633.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other|669.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|25.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|32.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|35.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|41.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|44.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||36.8|51.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|44.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|51.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|55.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|61.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|73.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|97.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||90.9|104.9|89.6|106.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|112.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||107.0|117.0|106.0|117.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White|112.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|104.91|120.11|121.6|103.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|23.8|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||19.4|28.2||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|38.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|77.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|81.2|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||76.4|86.0|75.5|86.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|83.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|84.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|92.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||89.0|96.0|88.0|96.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|126.9|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|137.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|145.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|188.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|181.04|196.15|197.6|179.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|278.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|286.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||269.0|303.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|311.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||298.0|324.9|295.6|327.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|356.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|398.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Female|All|423.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|37.2|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||31.7|42.7||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|94.9|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|99.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|99.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|107.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||102.2|113.1|101.1|114.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|110.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|123.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|138.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|193.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||188.0|198.0|187.0|199.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|204.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|196.66|212.7|214.2|195.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|205.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|274.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|292.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||274.7|310.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|294.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||280.8|308.0|278.3|310.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|374.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|464.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2011|Male|All|497.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|49.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||44.6|53.5||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|82.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|94.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|98.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|99.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||92.6|106.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|100.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||97.1|104.6|96.4|105.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|113.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|114.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|138.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|172.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||169.0|176.0|168.0|176.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|176.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|177.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|187.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|182.09|192.74|193.8|181.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|229.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|241.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||230.2|252.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|342.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||332.2|352.3|330.3|354.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|390.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|445.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|All|477.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|15.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|19.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||5.0|33.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|22.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|26.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|29.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||17.4|47.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|31.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|41.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|50.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||45.0|56.0|44.0|57.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|60.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI|88.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|166.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|210.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|260.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|345.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||290.5|400.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|427.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|450.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|419.29|481.29|487.2|413.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|465.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|528.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||500.2|556.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|542.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|551.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||532.0|570.0|528.0|573.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|568.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|687.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|949.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||917.8|981.3|911.9|987.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black|1015.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|28.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||17.6|38.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|33.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|40.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|56.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|67.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||51.0|87.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|79.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|86.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||71.0|104.8|68.3|108.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|109.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||105.0|113.0|104.0|114.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|122.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|163.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|179.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|184.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|177.84|191.6|192.9|176.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic|191.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|16.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|85.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||42.0|128.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|106.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||54.9|185.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|138.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|145.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|118.69|171.78|177.0|113.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|276.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|544.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other|667.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|33.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||27.4|40.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|36.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|44.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|46.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|46.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|62.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|77.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||70.1|84.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|82.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|103.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|108.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||100.8|115.5|99.5|117.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|140.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|140.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|132.29|149.21|150.8|130.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White|156.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||151.0|162.0|149.0|163.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|34.4|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||29.1|39.8||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|52.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|59.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||52.2|67.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|72.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|73.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|87.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||82.2|92.0|81.2|93.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|93.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|96.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||92.0|100.0|92.0|100.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|105.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|121.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|131.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|146.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|185.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|178.14|193.0|194.4|176.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|237.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||221.8|252.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|239.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|341.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||327.9|355.9|325.3|358.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|346.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|384.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|All|448.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|Asian/PI||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|Black|198.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|Hispanic|30.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Female|White|17.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|60.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||53.4|67.3||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|105.4|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|111.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|113.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||108.3|119.5|107.3|120.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|116.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|124.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|124.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|140.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||128.5|152.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|187.9|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|189.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|181.69|196.96|198.4|180.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|208.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|218.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|224.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|245.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||229.5|262.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|248.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||242.0|253.0|241.0|254.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|342.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||328.3|357.4|325.6|360.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|375.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|510.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|All|550.6|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|Asian/PI||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|Black|318.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|Hispanic|60.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2012|Male|White|59.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|52.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||48.0|57.3||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|89.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|93.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|95.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|104.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||67.1|111.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|112.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|116.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||112.1|120.1|111.3|120.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|122.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|160.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|164.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|166.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|161.43|171.38|172.3|160.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|190.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||186.0|193.0|186.0|194.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|190.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|231.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||220.0|242.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|267.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|333.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|356.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||346.4|366.8|344.5|368.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|413.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|All|472.3|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|10.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||0.2|21.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|18.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|26.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|30.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|33.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|34.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||20.9|52.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|46.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|49.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|49.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI|56.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||51.0|62.0|50.0|63.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|177.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|200.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|219.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|341.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||287.3|396.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|344.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|419.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|481.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|450.19|513.55|519.6|444.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|504.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||476.6|532.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|550.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|565.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|590.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|611.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||591.0|631.0|587.0|635.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|843.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black|940.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||909.1|971.7|903.3|977.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|35.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|41.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||28.9|54.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|50.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|56.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|74.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|86.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||67.7|108.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|106.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||88.9|125.9|85.9|129.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|117.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|119.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|123.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||119.0|127.0|118.0|128.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|156.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|150.37|162.91|164.1|149.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|175.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic|247.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|10.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|143.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|181.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||118.6|244.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|238.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|205.19|271.73|278.3|198.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|484.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|522.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other|837.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|37.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||31.0|44.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|41.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|43.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|45.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|48.9|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|51.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|65.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|85.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|86.2|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||78.9|93.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|96.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|101.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|107.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|100.17|114.9|116.3|98.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|137.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||129.3|145.8|127.8|147.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White|157.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||151.0|163.0|150.0|164.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|38.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||32.7|43.9||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|49.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||42.8|56.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|52.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|65.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|72.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|100.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||96.0|104.0|96.0|104.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|104.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||99.2|110.0|98.2|111.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|107.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|112.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|115.4|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|116.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|118.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|157.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|150.92|164.5|165.8|149.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|233.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||217.7|248.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|242.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|318.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|330.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||316.5|343.9|314.0|346.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|347.7|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|All|359.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|Asian/PI||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|Black|172.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|Hispanic|29.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Female|White|16.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|66.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||59.5|74.2||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|109.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|119.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|121.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|126.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|127.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||121.3|133.2|120.2|134.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|132.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|160.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||147.3|172.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|175.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|210.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|216.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|228.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||213.1|244.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|270.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|279.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||273.0|285.0|271.0|286.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|293.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|349.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|385.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||369.9|400.6|367.1|403.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|472.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|All|607.6|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|Asian/PI|36.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|Black|272.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|Hispanic|53.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2013|Male|White|61.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|91.1|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|94.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||88.4|100.8||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|105.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|120.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||113.0|128.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|133.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|141.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||137.2|146.1|136.4|146.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|144.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|145.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|164.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|167.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|167.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|162.81|172.69|173.6|161.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|184.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|224.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|226.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||222.0|230.0|221.0|230.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|273.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|311.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||299.1|324.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|316.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|358.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||348.1|368.5|346.1|370.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|380.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|All|390.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|14.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|16.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||3.3|29.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|16.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|19.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|22.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|32.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|32.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||20.1|50.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|38.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|49.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|51.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI|65.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||59.0|71.0|57.0|72.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|231.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|236.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|242.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|294.2|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||244.2|344.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|349.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|388.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|360.18|416.37|421.7|354.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|394.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|522.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|555.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|588.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|680.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||647.9|712.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|721.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||699.0|743.0|695.0|747.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|773.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black|920.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||889.8|951.3|884.1|957.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|50.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|50.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|62.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|73.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||56.0|90.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|92.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||77.0|111.3|74.2|114.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|97.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|100.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||80.8|124.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|101.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|139.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|143.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||139.0|148.0|138.0|149.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|154.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|166.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|160.07|172.84|174.1|158.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic|268.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|16.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|122.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||66.9|205.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|180.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|184.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|232.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||161.3|303.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|313.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|275.87|350.42|357.8|268.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|551.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other|1195.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|47.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|50.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|50.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||43.0|58.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|58.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|62.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|67.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|72.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|94.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||86.9|102.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|98.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|105.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|98.45|113.0|114.4|97.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|112.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|126.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|149.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||141.4|158.7|139.8|160.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White|208.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||202.0|215.0|200.0|217.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|52.2|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||45.1|59.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|57.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|63.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|65.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||58.6|73.3||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|88.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|94.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|107.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||103.0|111.0|103.0|112.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|116.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||110.9|122.3|109.8|123.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|126.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|127.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|128.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|130.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|151.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|240.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|277.2|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|298.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|301.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||283.9|318.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|324.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|All|335.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||322.2|349.8|319.7|352.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|Asian/PI|17.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|Black|184.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|Hispanic|36.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Female|White|33.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|123.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||113.0|133.0||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|126.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|137.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|144.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|163.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|166.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||159.3|172.8|158.0|174.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|184.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|177.3|192.08|193.5|175.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|190.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||176.8|204.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|193.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|241.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|246.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|308.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|323.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||304.3|341.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|328.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|336.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|344.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||337.0|351.0|336.0|352.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|382.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||367.3|397.8|364.5|400.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|464.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|All|482.4|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|Asian/PI|33.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|Black|396.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|Hispanic|62.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2014|Male|White|112.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|97.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|113.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|117.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||110.4|124.2||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|141.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|153.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||148.5|157.7|147.6|158.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|162.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|197.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|197.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||188.0|207.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|198.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|205.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|200.33|211.17|212.2|199.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|206.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|225.2|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|259.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||255.0|263.0|254.0|264.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|270.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|300.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|334.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||321.2|347.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|334.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|344.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||334.4|354.4|332.6|356.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|All|410.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|14.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|23.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|25.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|30.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||12.3|47.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|32.9|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|36.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|40.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|41.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|42.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||28.2|62.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|63.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI|80.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||73.0|87.0|72.0|88.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|226.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|238.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|272.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|381.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|418.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|389.66|447.12|452.6|384.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|422.0|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|555.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||487.3|623.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|596.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|604.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|621.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|698.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|713.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||680.5|746.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|748.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||726.0|770.0|722.0|775.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|761.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black|827.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||799.0|857.1|793.7|862.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|43.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|55.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|68.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|81.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|85.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||67.2|104.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|93.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||78.0|112.1|75.3|115.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|112.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|145.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|160.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|175.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||170.0|180.0|169.0|181.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|178.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||150.8|206.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|202.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|202.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|195.09|209.0|210.3|193.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|253.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic|290.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|52.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|37.1|66.87|69.8|34.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|86.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|88.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|214.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||138.8|316.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|236.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|260.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||185.5|336.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|698.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other|1122.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|57.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|58.4|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|59.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||51.4|68.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|70.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|83.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|86.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|99.5|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|100.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|112.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|133.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|143.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|158.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||149.0|168.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|166.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||157.5|175.8|155.9|177.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|185.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|175.8|195.01|196.9|174.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White|242.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||235.0|249.0|233.0|251.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|58.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|62.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|84.8|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||76.5|93.2||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|88.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|102.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|116.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||111.0|124.4|109.9|123.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|120.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||116.0|124.0|115.0|125.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|120.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||109.3|130.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|127.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|132.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|141.5|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|174.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|167.09|181.09|182.4|165.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|184.4|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|190.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|257.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|299.2|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|309.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|315.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||297.3|333.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|318.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||305.0|331.8|302.5|334.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|All|329.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|Asian/PI|45.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|Black|284.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|Hispanic|43.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Female|White|42.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|139.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|149.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||138.3|160.2||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|154.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|163.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|188.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||181.1|195.5|179.7|196.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|192.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|238.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|238.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|230.04|246.66|248.3|228.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|265.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|269.2|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|276.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||260.3|293.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|300.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|317.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|345.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|350.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|355.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Annual race/ethnicity rates for some categories may be underestimated. Between 15% to 20% of STD cases do not have racial classification.|||335.4|374.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|364.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|372.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||357.7|387.7|354.9|390.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|397.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||390.0|404.0|388.0|406.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|500.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|All|690.0|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|American Indian/Alaska Native|756.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|Asian/PI|43.1|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|Black|467.8|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|Hispanic|80.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2015|Male|White|118.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|113.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|133.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||126.6|141.3||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|147.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|151.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|186.7|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|188.4|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||183.3|193.6|182.4|194.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|201.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|212.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|225.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|220.28|231.54|232.6|219.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|243.9|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|246.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||235.4|257.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|309.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|313.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||308.0|317.0|307.0|318.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|338.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|392.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|425.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||414.5|436.7|412.5|438.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|455.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|456.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|All|631.0|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|26.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|38.7|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|46.4|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|53.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|55.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|58.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|66.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|82.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||62.1|107.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|94.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||87.0|102.0|83.0|103.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI|97.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|244.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|330.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|372.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|425.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|397.0|454.16|459.6|391.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|481.5|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|533.0|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|565.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|641.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||569.0|714.2
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|660.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|781.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||759.0|804.0|754.0|808.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|788.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|829.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|989.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||958.3|1021.3|952.4|1027.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black|1049.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|43.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|62.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|101.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|104.4|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|108.0|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|137.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||118.1|158.6|114.8|162.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|185.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|187.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|202.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||173.3|231.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|206.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|199.79|213.7|215.0|198.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|221.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||215.0|226.0|214.0|227.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|262.4|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic|364.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Hispanic||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other|30.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|19.17|41.48|43.7|17.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other|59.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other|213.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||138.3|315.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other|310.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other|1235.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other|1569.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other||Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Gender specific age groups and race/ethnicity calculatons exclude |Not Specified from the denominator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of |other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|52.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|71.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|74.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|85.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|112.7|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|114.7|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|130.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|150.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|160.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|193.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|197.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||186.9|208.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|216.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||205.8|226.8|203.9|228.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|247.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|236.22|258.39|260.5|234.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White|268.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||261.0|276.0|259.0|277.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Massachusetts Department of Public Health has suppressed this value due to low number of cases in the state of Massachusetts.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|66.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|77.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|83.4|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||75.1|91.6||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|90.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|100.3|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|124.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|138.5|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||132.3|144.7|131.1|145.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|138.8|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||127.3|150.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|144.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||140.0|149.0|139.0|149.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|160.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|179.0|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|181.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|174.15|188.32|189.7|172.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|195.7|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|217.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|315.9|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|327.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|336.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|379.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||364.9|394.0|362.2|396.9
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|All|396.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|American Indian/Alaska Native||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|Asian/PI|28.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|Black|230.3|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|Hispanic|84.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Female|White|56.4|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|162.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|170.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|184.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||171.8|196.1||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|212.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|237.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||229.0|245.2|227.5|246.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|244.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|271.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|263.04|280.63|282.3|261.4
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|275.5|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|292.2|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|355.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|OPHAT Gonorrhea query|||||337.2|374.3
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|356.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|436.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|459.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|462.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed cases of Gonorrhea Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|475.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||458.6|492.5|455.5|495.8
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|477.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||469.0|485.0|468.0|487.0
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|518.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|589.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|All|952.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|American Indian/Alaska Native|1491.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|Asian/PI|58.9|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|Black|667.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|Hispanic|155.6|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|Other||Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2016|Male|White|168.7|Long Beach, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|All|221.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|All|479.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||467.4|490.8|465.2|493.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Asian/PI|43.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Black|825.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Black|1112.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1079.6|1146.2|1073.4|1152.7
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Hispanic|196.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Hispanic|218.0|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||193.8|244.4|189.5|249.5
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|Other|2069.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|White|84.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Both|White|233.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||222.7|244.4|220.8|246.6
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Female|All|176.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Female|All|424.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||409.3|440.1|406.4|443.1
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Male|All|264.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Gonorrhea Rate (Per 100,000 People)|2017|Male|All|537.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||520.0|555.8|516.6|559.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|2.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.5|2.5|1.4|2.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|4.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|6.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||4.7|7.9||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|7.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|8.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|10.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|11.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||9.3|13.1|9.0|13.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|11.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|12.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||9.5|14.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|14.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|14.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|15.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|15.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|18.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|20.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|All|43.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||41.0|45.0|41.0|45.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI|0.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI|1.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI|2.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI|3.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI|8.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI|9.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|4.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.1|7.2|1.6|7.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|9.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|9.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|13.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|15.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|9.18|20.8|21.9|8.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|20.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|20.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|21.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|23.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|25.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|27.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|27.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||22.6|34.0|21.7|35.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|30.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||23.7|37.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|66.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black|73.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||66.0|80.0|64.0|81.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|1.8|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.8|2.7|0.7|2.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|2.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|4.2|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.1|8.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|7.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|9.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|9.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|9.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||6.1|13.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|10.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|11.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|11.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|13.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|11.26|15.01|15.4|10.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|26.9|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic|45.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||43.0|48.0|42.0|48.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other|4.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other|11.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other|16.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|2.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.3|2.8|1.1|2.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|2.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.6|3.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|2.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|4.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|6.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|7.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|8.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||4.7|11.5||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|8.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|9.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|18.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|23.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White|36.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||33.0|39.0|33.0|40.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|0.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|0.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|0.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|1.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.2|2.5
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|1.9|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|2.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|3.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|3.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|9.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All|12.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||11.0|13.0|10.0|13.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All||San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Rates censored where less than 5 cases were presented as data would be statistically unstable. Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Female|All||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|3.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.8|4.9|2.6|5.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|8.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|12.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||8.9|15.1||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|13.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|17.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|17.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|18.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|19.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||16.6|23.8|16.0|24.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|23.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||18.4|28.5
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|23.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|28.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|28.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2010 U.S. Census|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|29.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|36.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|41.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2010|Male|All|74.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||71.0|77.0|70.0|78.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|1.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.2|2.2|1.1|2.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|5.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||3.6|6.5||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|5.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|7.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|9.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|9.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||7.8|11.3|7.6|11.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|9.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|10.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|12.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|13.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|13.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|15.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|15.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||12.9|18.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|22.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|35.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|All|46.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||45.0|48.0|44.0|49.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI|3.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI|3.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI|14.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI|15.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||12.0|18.0|12.0|19.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|8.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|10.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|12.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|17.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|18.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|19.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|19.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||15.1|24.7|14.4|25.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|20.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|13.65|27.04|28.3|12.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|21.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|22.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|28.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|36.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||29.4|44.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|56.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black|89.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||81.0|97.0|80.0|98.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|1.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.7|2.5|0.5|2.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|5.4|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||2.8|8.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|8.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|8.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|8.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|9.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|12.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|10.39|13.96|14.3|10.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|27.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic|44.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||41.0|46.0|41.0|47.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other|8.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other|12.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other|26.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|3.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|5.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|5.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||2.9|8.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|6.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|6.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|7.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|7.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|10.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|15.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|16.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White|43.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||40.0|47.0|40.0|47.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|0.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|0.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|0.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|0.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|0.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|1.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|2.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.8|3.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|2.5|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|2.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|3.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|4.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|6.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All|10.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||9.0|11.0|9.0|11.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Female|All||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|3.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.3|4.2|2.1|4.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|9.5|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||6.7|12.2||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|10.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|14.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|17.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|17.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|17.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|17.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|15.29|20.0|20.5|14.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|18.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|18.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||15.2|22.2|14.7|22.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|22.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|25.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|25.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|29.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|30.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||24.3|35.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|41.9|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|71.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2011|Male|All|83.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||79.0|86.0|79.0|86.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|1.4|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.9|1.8|0.9|1.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|4.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|7.7|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||5.9|9.5||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|9.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||7.5|12.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|10.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|11.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|12.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|12.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||11.0|15.0|10.6|15.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|13.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|15.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|17.3|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|17.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|17.9|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||15.0|21.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|25.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|25.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|54.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||52.0|55.0|51.0|56.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|All|112.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|15.66|18.89|19.2|15.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|0.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|2.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|2.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|3.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|4.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI|17.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||14.0|20.0|13.0|20.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|12.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|12.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|17.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|18.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|18.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|20.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|22.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|23.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||18.7|29.1|17.9|30.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|24.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||18.5|30.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|31.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|31.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|49.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|12.57|25.28|26.5|11.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|52.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black|115.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||106.0|124.0|105.0|125.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|1.2|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.5|2.0|0.3|2.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|2.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|4.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|9.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|10.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|11.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|11.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|12.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|12.8|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||8.8|16.7||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|19.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|22.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|23.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|47.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||45.0|50.0|44.0|51.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic|95.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|19.77|24.54|25.0|19.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other|9.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other|11.5|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other|14.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other|14.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.; Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|1.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.2|2.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|1.5|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.8|2.2|0.7|2.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|3.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|4.4|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|6.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|6.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|8.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|9.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||7.4|11.9|7.0|12.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|10.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|11.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|12.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|15.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|23.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White|51.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||47.0|54.0|47.0|55.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|0.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|0.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|0.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|0.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|0.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|0.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|1.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|2.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.8|3.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|2.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|4.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|5.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|6.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All|8.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||7.0|10.0|7.0|10.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Female|All||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|2.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.9|3.6|1.7|3.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|7.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|14.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||11.5|18.4||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|17.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||13.2|21.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|20.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|21.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|22.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|24.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|24.4|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|25.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||21.3|29.4|20.6|30.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|26.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|29.1|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|34.3|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|35.4|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||29.7|42.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|47.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|51.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|67.9|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2012|Male|All|98.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||95.0|102.0|94.0|102.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|2.7|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.1|3.3|1.9|3.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|10.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||8.5|12.6||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|10.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|13.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|13.4|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||10.8|16.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|13.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|15.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|16.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|16.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|18.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|18.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|21.3|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||18.1|24.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|22.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|22.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|14.95|18.09|18.4|14.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|40.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|All|60.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||58.0|62.0|57.0|63.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|0.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|2.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|3.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|3.3|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|3.4|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.1|5.6|0.7|6.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|4.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||2.4|7.4||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|6.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|14.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI|20.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||16.0|23.0|16.0|24.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|2.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.7|4.5|0.3|4.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|17.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|17.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|20.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|22.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|24.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|25.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||20.3|30.9|19.4|32.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|25.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||19.5|32.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|26.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|26.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|29.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|35.9|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|41.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|43.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|78.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|18.79|33.56|35.0|17.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black|122.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||113.0|131.0|112.0|133.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|2.8|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.7|4.0|1.4|4.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|5.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|6.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||1.3|11.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|9.2|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||5.9|12.6||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|9.9|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|11.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|12.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|12.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|13.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|14.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|17.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|20.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|46.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|49.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|18.3|22.85|23.3|17.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic|54.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||51.0|57.0|51.0|57.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other|4.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other|7.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other|8.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|2.27|14.89|16.1|1.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other|16.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other|22.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other|22.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|2.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.7|3.4|1.5|3.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|4.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|7.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||4.4|10.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|7.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|8.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|8.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|9.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||7.4|11.9|7.1|12.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|10.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|10.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|11.8|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||7.7|15.9||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|11.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|13.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|14.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|22.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|40.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|52.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|5.52|9.39|9.8|5.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White|56.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||53.0|60.0|52.0|60.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|0.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|0.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|1.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||0.0|2.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|1.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|1.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|2.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|2.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|2.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|4.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|4.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|10.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||9.0|12.0|9.0|12.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All|28.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Female|All||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|0.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|5.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||4.1|6.5|3.9|6.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|10.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|20.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||16.0|24.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|21.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|23.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|26.6|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||21.2|32.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|28.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|28.6|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||24.6|33.2|23.9|34.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|29.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|30.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|35.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|36.4|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|42.5|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||36.2|49.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|43.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|59.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|79.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2013|Male|All|109.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||106.0|113.0|105.0|114.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|4.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||3.5|5.1|3.4|5.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|7.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||5.9|9.4||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|10.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||8.7|12.3|8.4|12.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|10.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|11.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|11.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|10.62|13.26|13.5|10.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|15.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|16.4|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|17.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||14.8|20.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|20.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|21.1|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||17.7|24.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|21.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|22.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|24.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|34.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|34.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|All|63.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||61.0|64.0|60.0|65.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|3.2|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|3.7|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|4.3|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|5.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|8.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|14.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI|22.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||18.0|26.0|18.0|26.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|8.2|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||4.8|11.6|4.2|12.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|20.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||16.1|25.7|15.4|26.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|21.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|21.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|23.2|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|24.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|17.69|31.88|33.2|16.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|25.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|28.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|33.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|36.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|41.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||33.3|49.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|45.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|66.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|67.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black|121.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||112.0|130.0|111.0|132.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|5.5|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||3.8|7.1|3.5|7.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|7.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|11.2|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||7.5|14.9||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|12.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|13.0|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|11.25|14.83|15.2|10.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|14.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|14.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|15.6|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|16.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|23.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|30.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|49.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic|59.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||56.0|61.0|55.0|62.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other|8.2|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|2.17|14.23|15.4|1.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other|14.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other|19.0|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other|21.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other|67.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|3.5|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.5|4.5|2.3|4.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|5.5|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||2.7|8.3||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|7.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|5.47|9.32|9.7|5.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|8.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|8.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|9.3|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|10.0|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||6.6|13.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|10.7|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|13.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|19.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|21.1|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|28.6|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White|58.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||54.0|61.0|53.0|62.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|0.7|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|1.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|1.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|2.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|3.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|3.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|4.7|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|3.51|5.82|6.0|3.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|6.2|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|6.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|7.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All|13.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||12.0|14.0|12.0|15.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Female|All||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|8.2|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||6.7|9.8|6.5|10.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|14.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||10.7|17.5||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|19.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|19.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|17.05|21.85|22.3|16.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|20.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||17.1|24.3|16.5|25.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|21.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|31.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|31.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|32.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|34.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||28.5|40.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|37.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|38.8|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|42.7|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||35.9|49.5
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|43.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|46.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|63.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|68.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2014|Male|All|110.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||106.0|114.0|105.0|114.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|4.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||3.3|4.7|3.1|4.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|8.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||6.3|10.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|11.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|11.8|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|12.0|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|15.0|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|15.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||13.8|18.2|13.4|18.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|17.8|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|18.5|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|20.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|20.7|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||17.6|24.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|21.8|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|23.4|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|24.3|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|26.5|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|28.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|31.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||27.8|36.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|41.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|All|77.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||75.0|79.0|74.0|80.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|0.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|3.4|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.1|5.6|0.7|6.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|3.6|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|4.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|5.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|6.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|8.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|14.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|14.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI|21.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||17.0|24.0|17.0|25.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|11.8|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||7.8|15.9|7.0|16.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|17.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|11.62|23.37|24.5|10.5
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|21.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|21.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||16.9|26.6|16.2|27.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|22.9|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|25.1|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|25.7|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|29.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|30.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|33.5|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|46.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|47.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|53.0|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|67.5|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||57.3|77.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|73.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black|152.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||142.0|162.0|140.0|164.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|3.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.1|4.6|1.8|4.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|8.1|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|12.1|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||8.3|16.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|13.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|13.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|13.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|12.06|15.71|16.1|11.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|16.0|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|16.4|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|16.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|17.5|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|17.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|23.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|54.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic|70.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||67.0|73.0|66.0|73.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other|7.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other|11.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other|24.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other|53.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. Suppressed due to counts <4.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.; Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|3.1|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||2.2|4.0|2.0|4.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|6.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|4.81|8.44|8.8|4.5
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|8.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|8.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|9.7|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|11.5|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|12.8|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|13.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||11.2|16.6|10.8|17.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|13.8|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|14.3|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||10.2|18.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|15.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|15.9|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|16.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|33.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|38.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White|76.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||72.0|80.0|71.0|81.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Both|White||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|0.0|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|0.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|1.0|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|2.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|2.8|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|2.9|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||1.2|4.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|2.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|3.0|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|3.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|4.4|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|4.8|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|9.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|10.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All|14.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||12.0|15.0|12.0|15.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Female|All||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|14.9|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||11.5|18.4||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|19.5|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Department of State Health Services, Sexual Transmitted Disease Data|Cases: Texas Department of State Health Services, Sexual Transmitted Disease Data; Population: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/counties/asrh/cc-est2016-alldata-48.csv,|Bexar County level data||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|20.8|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|21.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|28.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|30.8|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||26.6|35.4|25.9|36.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|35.0|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|35.9|New York City, NY|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|NYC DOHMH Bureau of Sexually Transmitted Diseases Control |Denominator source is NYC DOHMH population estimates, modified from US Census Bureau intercensal population estimates.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|37.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|39.1|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||33.2|45.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|39.6|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|42.6|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|44.7|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|44.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|56.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|62.8|Charlotte, NC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Population Estimates:  US Census 2010 Estimates, City of Charlotte, NC STD Data: North Carolina Electronic Disease Surveillance System, Mecklenburg County Data|||||54.5|71.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|84.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2015|Male|All|139.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||135.0|144.0|134.0|144.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|6.3|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||5.4|7.2|5.2|7.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|11.2|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|13.7|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|14.4|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||12.5|16.7|12.1|17.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|14.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|15.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|16.0|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||13.4|18.5||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|19.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|21.0|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||18.0|24.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|23.6|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|25.2|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|28.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|29.1|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|30.1|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|49.2|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All|91.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||89.0|94.0|88.0|94.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|All||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|3.8|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|3.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||1.5|6.4|1.0|6.9
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|4.2|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|6.6|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|7.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|8.3|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|19.6|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|25.4|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI|32.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||27.0|36.0|27.0|37.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Asian/PI||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|18.1|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|19.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||13.9|24.1|12.9|25.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|21.6|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|25.6|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|27.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||22.2|33.0|21.3|34.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|32.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|34.4|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|39.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|41.2|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|43.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|58.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|74.9|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black|175.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||165.0|186.0|163.0|188.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Black||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|6.0|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||4.3|7.7|4.0|8.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|9.3|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|9.9|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|17.9|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|18.8|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|19.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|21.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|23.6|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||18.2|29.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|25.1|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|32.6|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|38.2|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|72.3|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic|86.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||82.0|89.0|82.0|90.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Hispanic||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other|14.7|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other|20.6|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other|34.1|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county. Other Race/Ethnicity includes those of other race| American Indian and Alaskan Native descent| 2 or more races| and those of unknown race/ethnicity.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other|57.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|| Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Gender specific age groups and race/ethnicity calculatons exclude |Not Specified from the denominator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. Data includes rates for Bexar County and San Antonio.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|Other||San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator. ||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|4.4|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||3.3|5.5|3.0|5.7
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|7.9|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|5.91|9.88|10.3|5.5
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|9.5|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||7.4|11.9|7.1|12.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|10.1|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|10.9|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|11.0|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|11.8|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||7.7|15.9||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|13.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|13.7|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|16.7|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|18.3|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|22.5|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|23.8|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|34.8|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|35.5|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White|82.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||78.0|86.0|77.0|87.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Both|White||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|0.6|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||0.2|1.0|0.1|1.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|1.2|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|2.2|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|2.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|2.7|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|3.1|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|3.1|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|3.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|3.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|2.58|4.58|4.8|2.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|4.5|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||2.6|6.4||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|7.0|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|9.0|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|11.4|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All|18.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||16.0|19.0|16.0|20.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.|1.6|4.2|1.4|4.6
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Female|All||Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|11.9|Las Vegas (Clark County), NV|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||10.1|13.7|9.8|14.1
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|19.6|San Antonio, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Data includes rates for Bexar County and San Antonio.|17.2|21.92|22.4|16.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|20.6|Oakland (Alameda County), CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|California Department of Public Health, STD Control Branch||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|24.6|Dallas, TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Data Sources: Dallas County Department of Health and Human Services; Texas Department of State Health Services; Census Population: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF||All data are at the Dallas County level.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|26.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|27.1|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||23.2|31.5|22.5|32.4
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|27.3|San Jose, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Santa Clara County Public Health Department, California Reportable Diease Information Exchange, 2010-2016, data are provisional as of December 19, 2017; 2010 U.S. Census|||22.6|32.0||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|30.5|San Diego County, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|County of San Diego, Health and Human Services Agency, Public Health Services, Epidemiology and Immunizations Services Branch; January, 2018; SANDAG Current Population Estimates, Received May 2017.|Annual case counts are grouped by CDC disease year, following the schema of CDC disease weeks, which do not necessarily reflect the calendar year. Cases are grouped into year on the basis of the earliest of the following dates: onset date, laboratory specimen collection date, diagnosis date, death date, date report was received. Rates are calculated per 100,000 population using the appropriate population stratifications and year population estimates as received by the San Diego Association of Governments (SANDAG) which is our county's official census holder.|Confirmed cases only. Cases include non-residents and those of unknown residence who received diagnosis while in the county.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|37.9|Miami (Miami-Dade County), FL|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Florida Health Charts||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|39.6|Portland (Multnomah County), OR|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Orpheus|||||33.7|46.3
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|41.1|Columbus, OH|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Ohio Disease Reporting System.  Analysis by Office of Epidemiology, Columbus Public Health|Confirmed and Probable cases of primary and secondary Syphilis. Population 2011-2015,  ACS, U.S. Census.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|42.1|Fort Worth (Tarrant County), TX|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Texas Notifiable Conditions||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|47.9|Kansas City, MO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|54.9|Philadelphia, PA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Division of Disease Control, Philadelphia Dept of Public Health||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|59.5|Washington, DC|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|District of Columbia Public Health Information System (STD Surveillance Data); 2010 U.S. Census (Population Data)||||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|64.3|Denver, CO|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Patient Reporting Investigating Surveillance Manager (PRISM)||This data analysis was provided through a collaborative effort between the Denver Department of Public Health and Environment and Denver Public Health, a division of the Denver Health and Hospital Authority.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|98.7|Boston, MA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Massachusetts Department of Public Health Bureau of Infectious Disease and Laboratory Sciences, Division of STD Prevention||The 2013 data are current as of July 28, 2014, the 2014 data are curerent as of August 3, 2015, and the 2015 data are current as of September 26, 2016 and 2016 data is current as of June 30, 2017 and subject to change.; Syphilis includes those diagnosed in the primary, secondary stage and those diagnosed within the first year of infection.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2016|Male|All|162.0|Los Angeles, CA|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|STD Surveillance Database|||158.0|167.0|157.0|168.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|All|15.4|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County pop = 3,817,117.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|All|16.9|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||14.8|19.3|14.4|19.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|American Indian/Alaska Native||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Asian/PI|8.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Asian/Pacific Islander pop = 135,024.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Asian/PI||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Black|32.3|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||26.8|38.5|25.9|39.8
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Black|48.5|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Black pop = 177,490.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Hispanic|18.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Hispanic pop = 1,128,741.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Hispanic||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|Other|16.9|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Other pop = 135,807.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|White|10.2|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||8.0|12.7|7.7|13.2
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Both|White|11.3|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County White pop = 2,240,055.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Female|All|4.2|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Female pop = 1,928,652.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Female|All||Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|||Records where the value is blank in the data table indicate that the data are suppressed due to small counts, inadequate sample size, or unreliable parameter estimates such as relative standard error or confidence intervals. The reasons cities have suppressed data vary by indicator.|1.3|3.7|1.1|4.0
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Male|All|26.8|Phoenix, AZ|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.|Prism|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figure: Maricopa County Male pop = 1,888,465.|||||
Sexually Transmitted Infections|Primary and Secondary Syphilis Rate (Per 100,000 People)|2017|Male|All|32.7|Indianapolis (Marion County), IN|Incidence reported as a crude rate per 100,000 people, using 2010 U.S. Census figures.||||28.5|37.5|27.7|38.4
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|23600.0|Detroit, MI|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|24257.0|Cleveland, OH|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|35386.0|Philadelphia, PA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|39241.0|Baltimore, MD|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|41354.0|Dallas, TX|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|41877.0|Kansas City, MO|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|44153.0|Phoenix, AZ|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|45214.0|Chicago, Il|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|45524.0|San Antonio, TX|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|46803.0|Los Angeles, CA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|47604.0|Minneapolis, MN|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|48196.0|Oakland (Alameda County), CA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|50488.0|Denver, CO|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|50895.0|New York City, NY|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|51642.0|Boston, MA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|52430.0|Houston, TX|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|64473.0|Seattle, WA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|66583.0|Washington, DC|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|73012.0|San Francisco, CA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2012|Both|All|80090.0|San Jose, CA|Median household income (in 2012 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2012 Inflation-Adjusted Dollars)||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|24820.0|Detroit, MI|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|26096.0|Cleveland, OH|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|36836.0|Philadelphia, PA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|41978.0|Dallas, TX|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|42250.0|U.S. Total, U.S. Total|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|42266.0|Baltimore, MD|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|43100.0|Miami (Miami-Dade County), FL|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|45353.0|Houston, TX|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|45399.0|San Antonio, TX|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|45551.0|Kansas City, MO|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|46601.0|Phoenix, AZ|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|47099.0|Chicago, Il|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|48466.0|Los Angeles, CA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|50563.0|Minneapolis, MN|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|51089.0|Denver, CO|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|52223.0|New York City, NY|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|52873.0|Las Vegas (Clark County), NV|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|53583.0|Boston, MA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|54394.0|Oakland (Alameda County), CA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|56853.0|Fort Worth (Tarrant County), TX|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|67572.0|Washington, DC|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|67753.0|San Diego County, CA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|70172.0|Seattle, WA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|77485.0|San Francisco, CA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2013|Both|All|80977.0|San Jose, CA|Median household income (in 2013 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2013 Inflation-Adjusted Dollars)||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|24701.0|Cleveland, OH|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|25769.0|Detroit, MI|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|39043.0|Philadelphia, PA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|42557.0|Indianapolis (Marion County), IN|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|42665.0|Baltimore, MD|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|42926.0|Miami (Miami-Dade County), FL|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|44173.0|Kansas City, MO|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|45339.0|San Antonio, TX|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|45460.0|Houston, TX|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|47929.0|Phoenix, AZ|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|48734.0|Chicago, Il|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|50076.0|Dallas, TX|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|50544.0|Los Angeles, CA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|50791.0|Minneapolis, MN|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|51214.0|Las Vegas (Clark County), NV|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|52996.0|New York City, NY|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|53180.0|Columbus, OH|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|53657.0|U.S. Total, U.S. Total|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|53660.0|Portland (Multnomah County), OR|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|54511.0|Long Beach, CA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|54941.0|Denver, CO|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|56188.0|Oakland (Alameda County), CA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|56902.0|Boston, MA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|58099.0|Fort Worth (Tarrant County), TX|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|58841.0|Charlotte, NC|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|61787.0|Austin, TX|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|66192.0|San Diego County, CA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|70975.0|Seattle, WA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|71648.0|Washington, DC|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|85070.0|San Francisco, CA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2014|Both|All|87210.0|San Jose, CA|Median household income (in 2014 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2014 Inflation-Adjusted Dollars)||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|25980.0|Detroit, MI|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|28831.0|Cleveland, OH|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|41226.0|Indianapolis (Marion County), IN|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|41233.0|Philadelphia, PA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|43786.0|Miami (Miami-Dade County), FL|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|44165.0|Baltimore, MD|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|48064.0|Houston, TX|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|48452.0|Phoenix, AZ|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|48869.0|San Antonio, TX|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|50259.0|Kansas City, MO|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|50702.0|Chicago, Il|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|51552.0|Las Vegas (Clark County), NV|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|51799.0|Dallas, TX|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|52024.0|Los Angeles, CA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|53882.0|Columbus, OH|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|54571.0|Minneapolis, MN|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|54971.0|Long Beach, CA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|55752.0|New York City, NY|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|55775.0|U.S. Total, U.S. Total|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|56883.0|Charlotte, NC|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|58003.0|Denver, CO|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|58263.0|Boston, MA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|58807.0|Oakland (Alameda County), CA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|59231.0|Portland (Multnomah County), OR|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|60737.0|Fort Worth (Tarrant County), TX|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|65269.0|Austin, TX|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|67320.0|San Diego County, CA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|75628.0|Washington, DC|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|80349.0|Seattle, WA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|91451.0|San Jose, CA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2015|Both|All|92094.0|San Francisco, CA|Median household income (in 2015 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2015 Inflation-Adjusted Dollars)||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|27551.0|Cleveland, OH|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|28099.0|Detroit, MI|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|41449.0|Philadelphia, PA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|44874.0|Indianapolis (Marion County), IN|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|45935.0|Miami (Miami-Dade County), FL|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|47350.0|Baltimore, MD|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|47793.0|Houston, TX|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|49268.0|San Antonio, TX|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|51235.0|Kansas City, MO|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|52062.0|Phoenix, AZ|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|53006.0|Chicago, Il|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|54384.0|Las Vegas (Clark County), NV|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|54399.0|Dallas, TX|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|54432.0|Los Angeles, CA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|56055.0|Columbus, OH|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|56255.0|Minneapolis, MN|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|57617.0|U.S. Total, U.S. Total|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|58856.0|New York City, NY|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|60075.0|Long Beach, CA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|61105.0|Denver, CO|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|61534.0|Fort Worth (Tarrant County), TX|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|62629.0|Portland (Multnomah County), OR|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|62978.0|Charlotte, NC|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|63621.0|Boston, MA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|68060.0|Oakland (Alameda County), CA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|70158.0|Austin, TX|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|70824.0|San Diego County, CA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|75506.0|Washington, DC|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|83476.0|Seattle, WA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|101940.0|San Jose, CA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Median Household Income (Dollars)|2016|Both|All|103801.0|San Francisco, CA|Median household income (in 2016 inflation-adjusted dollars) using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar. Provide three most recent years of data. Notate your source.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1903 - Income in the Past 12 Months (In 2016 Inflation-Adjusted Dollars)||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|27.4|Seattle, WA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|29.1|San Jose, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|29.7|San Francisco, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|32.7|Washington, DC|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|38.3|Denver, CO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|38.9|Boston, MA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|40.3|Las Vegas (Clark County), NV|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|41.0|Kansas City, MO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|41.3|New York City, NY|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|41.6|Fort Worth (Tarrant County), TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|41.8|Minneapolis, MN|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|42.6|Oakland (Alameda County), CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|43.4|San Antonio, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|45.4|Chicago, Il|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|46.4|Los Angeles, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|46.6|Baltimore, MD|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|47.4|Phoenix, AZ|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|47.8|Houston, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|49.7|Philadelphia, PA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|51.1|Dallas, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|59.6|Miami (Miami-Dade County), FL|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|63.0|Cleveland, OH|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2012|Both|All|65.3|Detroit, MI|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|25.5|Seattle, WA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|28.4|San Francisco, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|28.8|San Jose, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|32.7|San Diego County, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|33.6|Washington, DC|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|34.0|U.S. Total, U.S. Total|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|37.5|Boston, MA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|38.6|Denver, CO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|39.3|Las Vegas (Clark County), NV|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|40.2|Kansas City, MO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|40.5|New York City, NY|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|40.9|Fort Worth (Tarrant County), TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|40.9|Minneapolis, MN|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|41.5|Oakland (Alameda County), CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|43.4|Baltimore, MD|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|43.9|Chicago, Il|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|44.3|San Antonio, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|46.4|Houston, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|46.5|Phoenix, AZ|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|46.8|Los Angeles, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|49.3|Philadelphia, PA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|50.8|Dallas, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|56.1|Miami (Miami-Dade County), FL|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|60.7|Cleveland, OH|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2013|Both|All|64.0|Detroit, MI|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|9.3|San Jose, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|11.7|Fort Worth (Tarrant County), TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|12.2|Austin, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|12.3|San Francisco, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|12.5|Seattle, WA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|13.4|San Diego County, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|13.5|Charlotte, NC|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|13.6|Denver, CO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|15.3|Dallas, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|15.3|San Antonio, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|15.5|Washington, DC|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|15.8|Columbus, OH|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|16.3|Portland (Multnomah County), OR|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|16.8|Long Beach, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|16.9|Miami (Miami-Dade County), FL|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|17.1|Houston, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|17.6|New York City, NY|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|18.1|Indianapolis (Marion County), IN|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|18.1|Minneapolis, MN|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|18.3|Boston, MA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|18.4|Chicago, Il|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|18.4|Los Angeles, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|18.4|Oakland (Alameda County), CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|19.9|Phoenix, AZ|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|20.5|Baltimore, MD|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|23.0|Philadelphia, PA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|32.7|Cleveland, OH|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2015|Both|All|36.9|Detroit, MI|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|9.9|San Francisco, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|10.0|San Jose, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|10.7|Charlotte, NC|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|11.2|Seattle, WA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|11.5|Austin, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|11.7|Fort Worth (Tarrant County), TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|11.7|San Diego County, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|12.5|Denver, CO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|13.2|U.S. Total, U.S. Total|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|13.4|Las Vegas (Clark County), NV|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|13.5|Dallas, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|13.7|Portland (Multnomah County), OR|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|14.6|Kansas City, MO|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|15.2|Columbus, OH|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|15.4|Miami (Miami-Dade County), FL|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|15.6|Oakland (Alameda County), CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|16.4|Indianapolis (Marion County), IN|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|16.4|San Antonio, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|16.5|New York City, NY|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|16.7|Chicago, Il|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|16.8|Long Beach, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|17.1|Houston, TX|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|17.2|Phoenix, AZ|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|17.5|Los Angeles, CA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|17.6|Washington, DC|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|18.5|Boston, MA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|19.5|Minneapolis, MN|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|19.6|Baltimore, MD|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|23.4|Philadelphia, PA|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|32.1|Cleveland, OH|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent Living Below 200% Poverty Level|2016|Both|All|32.8|Detroit, MI|Percentage of the population 200 percent below the level of poverty using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1701, Poverty Status in the Past 12 Months||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|29.0|Las Vegas (Clark County), NV|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|25.7|32.3||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|30.9|Phoenix, AZ|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|27.3|34.5||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|33.3|Kansas City, MO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2938000 was used to isolate data for Kansas City, MO.|27.5|39.1||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|36.3|San Antonio, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4865000 was used to isolate data for San Antonio, TX.|32.2|40.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|41.3|Cleveland, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3916000 was used to isolate data for Cleveland, OH.|34.0|48.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|42.6|Houston, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4835000 was used to isolate data for Houston, TX.|39.2|46.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|45.2|Fort Worth (Tarrant County), TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|41.4|49.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|45.6|Minneapolis, MN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|38.5|52.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|45.9|Detroit, MI|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2622000 was used to isolate data for Detroit, MI.|40.8|51.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|47.7|U.S. Total, U.S. Total|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools|47.4|48.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|48.4|Philadelphia, PA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|43.2|53.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|49.4|San Diego County, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 06073 was used to isolate data for San Diego County, CA.|46.2|52.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|50.3|Baltimore, MD|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools;  FIPS code 2404000 was used to isolate data for Baltimore, MD.|42.7|57.9||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|53.4|Seattle, WA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 5363000 was used to isolate data for Seattle, WA.|46.0|60.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|54.5|Los Angeles, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|52.2|56.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|54.6|San Jose, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0668000 was used to isolate data for San Jose, CA.|50.0|59.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|54.9|Chicago, Il|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|51.6|58.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|55.5|Dallas, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48113 was used to isolate data for Dallas county, TX.|48.8|62.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|55.5|Denver, CO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0820000 was used to isolate data for Denver, CO.|48.8|62.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|57.6|Long Beach, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0643000 was used to isolate data for Long Beach, CA.|49.9|65.3||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|58.6|Miami (Miami-Dade County), FL|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|54.7|62.5||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|59.2|Boston, MA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2507000 was used to isolate data for Boston, MA.|52.0|66.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|59.5|New York City, NY|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3651000 was used to isolate data for New York City, NY.|57.8|61.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|62.2|Oakland (Alameda County), CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0653000 was used to isolate data for Oakland, CA.|53.8|70.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|72.5|San Francisco, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0667000 was used to isolate data for San Francisco, CA.|65.0|80.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2012|Both|All|78.4|Washington, DC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 1150000 was used to isolate data for Washington, DC.|74.0|82.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|29.6|Phoenix, AZ|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|25.9|33.3||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|31.8|Las Vegas (Clark County), NV|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|28.3|35.3||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|34.6|Fort Worth (Tarrant County), TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|30.9|38.3||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|36.3|Dallas, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48113 was used to isolate data for Dallas county, TX.|33.6|39.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|40.8|Houston, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4835000 was used to isolate data for Houston, TX.|37.2|44.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|40.8|Kansas City, MO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2938000 was used to isolate data for Kansas City, MO.|32.2|49.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|41.0|Detroit, MI|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2622000 was used to isolate data for Detroit, MI.|35.0|47.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|41.2|San Antonio, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4865000 was used to isolate data for San Antonio, TX.|36.4|46.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|41.4|Cleveland, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3916000 was used to isolate data for Cleveland, OH.|33.3|49.5||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|41.5|Long Beach, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0643000 was used to isolate data for Long Beach, CA.|34.3|48.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|44.8|Philadelphia, PA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|40.0|49.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|45.9|San Diego County, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 06073 was used to isolate data for San Diego County, CA.|42.9|48.9||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|46.1|U.S. Total, U.S. Total|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools|45.8|46.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|46.7|San Jose, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0668000 was used to isolate data for San Jose, CA.|41.6|51.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|48.2|Minneapolis, MN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|40.2|56.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|50.9|Baltimore, MD|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools;  FIPS code 2404000 was used to isolate data for Baltimore, MD.|44.9|56.9||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|54.2|Los Angeles, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|51.4|57.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|55.8|Denver, CO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0820000 was used to isolate data for Denver, CO.|48.8|62.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|56.1|Chicago, Il|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|53.0|59.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|58.0|Oakland (Alameda County), CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0653000 was used to isolate data for Oakland, CA.|49.7|66.3||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|58.9|Miami (Miami-Dade County), FL|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|54.4|63.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|59.3|Boston, MA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2507000 was used to isolate data for Boston, MA.|52.5|66.1||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|60.2|New York City, NY|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3651000 was used to isolate data for New York City, NY.|58.6|61.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|68.3|Seattle, WA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 5363000 was used to isolate data for Seattle, WA.|61.7|74.9||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|70.9|San Francisco, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0667000 was used to isolate data for San Francisco, CA.|63.8|78.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2013|Both|All|78.5|Washington, DC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 1150000 was used to isolate data for Washington, DC.|72.1|84.9||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|28.2|Phoenix, AZ|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|25.2|31.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|32.9|Las Vegas (Clark County), NV|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|29.4|36.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|36.1|San Antonio, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4865000 was used to isolate data for San Antonio, TX.|31.5|40.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|38.9|Kansas City, MO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2938000 was used to isolate data for Kansas City, MO.|31.1|46.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|40.1|Fort Worth (Tarrant County), TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|35.5|44.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|40.5|Dallas, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48113 was used to isolate data for Dallas county, TX.|37.4|43.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|41.9|Indianapolis (Marion County), IN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|42.5|Cleveland, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3916000 was used to isolate data for Cleveland, OH.|34.6|50.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|43.6|Detroit, MI|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2622000 was used to isolate data for Detroit, MI.|37.4|49.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|43.9|Columbus, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|44.5|Houston, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4835000 was used to isolate data for Houston, TX.|41.1|47.9||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|45.8|San Jose, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0668000 was used to isolate data for San Jose, CA.|40.1|51.5||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|45.9|Philadelphia, PA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|40.7|51.1||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|46.1|Austin, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|47.1|U.S. Total, U.S. Total|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools|46.8|47.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|49.4|Minneapolis, MN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|42.8|56.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|50.4|San Diego County, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 06073 was used to isolate data for San Diego County, CA.|46.8|54.0||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|51.9|Long Beach, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0643000 was used to isolate data for Long Beach, CA.|44.6|59.2||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|52.4|Baltimore, MD|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools;  FIPS code 2404000 was used to isolate data for Baltimore, MD.|45.7|59.1||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|54.4|Oakland (Alameda County), CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0653000 was used to isolate data for Oakland, CA.|48.1|60.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|55.2|Charlotte, NC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|55.6|Los Angeles, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|53.4|57.8||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|57.0|Chicago, Il|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|54.3|59.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|59.7|Boston, MA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2507000 was used to isolate data for Boston, MA.|52.0|67.4||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|60.9|New York City, NY|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3651000 was used to isolate data for New York City, NY.|59.2|62.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|61.1|Miami (Miami-Dade County), FL|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|57.5|64.7||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|62.0|Seattle, WA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 5363000 was used to isolate data for Seattle, WA.|54.4|69.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|65.3|Denver, CO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0820000 was used to isolate data for Denver, CO.|58.0|72.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|71.7|San Francisco, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0667000 was used to isolate data for San Francisco, CA.|64.8|78.6||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2014|Both|All|86.4|Washington, DC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 1150000 was used to isolate data for Washington, DC.|82.3|90.5||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|32.9|Phoenix, AZ|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|33.1|Las Vegas (Clark County), NV|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|36.1|Indianapolis (Marion County), IN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|39.3|Dallas, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|40.2|Houston, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|40.7|Columbus, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|41.5|Cleveland, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|42.5|Charlotte, NC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|43.8|Fort Worth (Tarrant County), TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|45.7|Kansas City, MO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|47.0|San Antonio, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|47.6|U.S. Total, U.S. Total|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|47.9|Austin, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|49.5|Oakland (Alameda County), CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|50.3|San Diego County, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|54.0|Portland (Multnomah County), OR|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|55.4|San Jose, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|55.8|Long Beach, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|56.3|Chicago, Il|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|56.6|Los Angeles, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|57.1|Baltimore, MD|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools;  FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|57.6|Minneapolis, MN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|58.5|Boston, MA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|60.1|Denver, CO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|60.9|Miami (Miami-Dade County), FL|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|61.4|New York City, NY|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|69.3|Seattle, WA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|69.7|San Francisco, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2015|Both|All|74.6|Washington, DC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|35.3|Phoenix, AZ|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|37.3|Fort Worth (Tarrant County), TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|38.0|Las Vegas (Clark County), NV|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|38.6|Detroit, MI|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|39.0|Columbus, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|41.1|Kansas City, MO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|41.9|Houston, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|42.1|Dallas, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|48.0|Long Beach, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|48.0|U.S. Total, U.S. Total|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|48.1|Baltimore, MD|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools;  FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|48.8|Charlotte, NC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|50.7|San Antonio, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|51.4|Philadelphia, PA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|52.2|Cleveland, OH|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|52.3|Austin, TX|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|52.7|San Diego County, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|52.8|Indianapolis (Marion County), IN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|52.8|Minneapolis, MN|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|53.3|Denver, CO|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|54.4|Portland (Multnomah County), OR|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|56.2|Los Angeles, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|56.2|San Jose, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|58.7|Boston, MA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|60.2|Miami (Miami-Dade County), FL|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|60.9|Chicago, Il|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|61.0|Oakland (Alameda County), CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|63.6|New York City, NY|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|65.8|Seattle, WA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|67.3|San Francisco, CA|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of 3 and 4 Year Olds Currently Enrolled in Preschool|2016|Both|All|77.7|Washington, DC|Percent of age group 3 and 4 years enrolled in school using US Census Bureau, American Community Survey 1-year estimates. Includes enrollment in public and private schools.|US Census Bureau, American Community Survey 1-year estimates; Table ID S1401 - School Enrollment||Includes public & private schools; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|14.8|Seattle, WA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|15.6|San Jose, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|16.7|San Francisco, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|21.8|U.S. Total, U.S. Total|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|24.8|Washington, DC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|27.5|Boston, MA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|28.5|Denver, CO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|31.7|New York City, NY|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|32.7|Oakland (Alameda County), CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|33.0|Minneapolis, MN|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|34.1|Baltimore, MD|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|34.2|San Antonio, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|34.4|Chicago, Il|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|34.6|Kansas City, MO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|34.8|Los Angeles, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|37.6|Houston, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|37.8|Philadelphia, PA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|38.0|Phoenix, AZ|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|38.5|Dallas, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|55.7|Cleveland, OH|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Children Living in Poverty|2012|Both|All|61.4|Detroit, MI|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|15.7|Seattle, WA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|16.6|San Jose, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|18.6|San Francisco, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|18.7|San Diego County, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|19.9|U.S. Total, U.S. Total|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|22.0|Las Vegas (Clark County), NV|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|22.1|Fort Worth (Tarrant County), TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|24.6|Oakland (Alameda County), CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|25.4|Washington, DC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|26.6|Miami (Miami-Dade County), FL|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|28.0|Kansas City, MO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|29.2|New York City, NY|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|30.0|San Antonio, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|30.4|Boston, MA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|31.1|Denver, CO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|33.9|Chicago, Il|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|34.0|Los Angeles, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|35.7|Phoenix, AZ|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|35.8|Houston, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|36.1|Baltimore, MD|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|37.1|Philadelphia, PA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|39.4|Dallas, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|58.5|Cleveland, OH|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Children Living in Poverty|2013|Both|All|60.6|Detroit, MI|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Children Living in Poverty|2014|Both|All|1.6|San Francisco, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2014|Both|All|2.3|San Jose, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2014|Both|All|2.5|Seattle, WA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2014|Both|All|4.3|San Diego County, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2014|Both|All|4.7|Washington, DC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Children Living in Poverty|2014|Both|All|8.1|San Antonio, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|1.5|San Francisco, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|1.5|Seattle, WA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|2.4|San Jose, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|3.5|Portland (Multnomah County), OR|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|3.9|San Diego County, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|4.2|Austin, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|4.6|Charlotte, NC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|4.7|Washington, DC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|4.8|U.S. Total, U.S. Total|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|4.9|Denver, CO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|5.1|Fort Worth (Tarrant County), TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|5.2|Boston, MA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|5.4|Minneapolis, MN|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|5.6|Miami (Miami-Dade County), FL|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|5.8|Oakland (Alameda County), CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|5.9|Columbus, OH|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|6.0|Kansas City, MO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|6.0|New York City, NY|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|6.4|Los Angeles, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|6.7|Long Beach, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|6.7|San Antonio, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|6.8|Chicago, Il|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|7.3|Dallas, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|7.4|Baltimore, MD|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|8.0|Indianapolis (Marion County), IN|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|8.5|Phoenix, AZ|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|8.6|Philadelphia, PA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|8.9|Houston, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|11.4|Cleveland, OH|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Children Living in Poverty|2015|Both|All|14.5|Detroit, MI|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|1.2|San Francisco, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|2.2|Seattle, WA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|2.9|San Jose, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|3.6|San Diego County, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|3.8|Austin, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|3.8|Portland (Multnomah County), OR|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|4.1|Denver, CO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|4.4|Charlotte, NC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|4.5|U.S. Total, U.S. Total|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|4.7|Las Vegas (Clark County), NV|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|4.7|Washington, DC|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.1|Miami (Miami-Dade County), FL|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.2|Boston, MA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.4|Fort Worth (Tarrant County), TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.4|Minneapolis, MN|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.6|New York City, NY|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.8|Columbus, OH|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|5.9|Kansas City, MO|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.0|Los Angeles, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.0|Oakland (Alameda County), CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.1|Chicago, Il|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.5|Long Beach, CA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.7|San Antonio, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.8|Dallas, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|6.9|Baltimore, MD|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|7.1|Indianapolis (Marion County), IN|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|8.2|Phoenix, AZ|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|8.3|Houston, TX|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|8.3|Philadelphia, PA|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|11.6|Cleveland, OH|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Children Living in Poverty|2016|Both|All|13.0|Detroit, MI|Percentage of the population under 18 years of age below the poverty level using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B17001 - Poverty Status in the Past 12 Months by Sex by Age||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|74.8|Dallas, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|75.8|Cleveland, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|75.9|Houston, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|76.3|Los Angeles, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|76.8|Detroit, MI|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|80.2|Phoenix, AZ|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|80.3|New York City, NY|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|80.8|Baltimore, MD|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|81.0|Oakland (Alameda County), CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|81.2|San Antonio, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|81.5|Chicago, Il|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|81.8|San Jose, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|81.9|Philadelphia, PA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|85.7|Denver, CO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|86.4|Boston, MA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|87.0|San Francisco, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|88.0|Minneapolis, MN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|88.6|Washington, DC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|88.7|Kansas City, MO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2012|Both|All|93.7|Seattle, WA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|75.6|Dallas, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|75.9|Los Angeles, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|77.6|Houston, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|78.0|Cleveland, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|78.2|Detroit, MI|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|78.8|Miami (Miami-Dade County), FL|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|80.6|Phoenix, AZ|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|80.9|New York City, NY|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|81.3|Oakland (Alameda County), CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|82.2|Baltimore, MD|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|82.3|San Jose, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|82.4|Chicago, Il|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|82.5|San Antonio, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|82.6|Philadelphia, PA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|83.9|Las Vegas (Clark County), NV|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|84.5|Fort Worth (Tarrant County), TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|85.8|San Diego County, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|86.0|U.S. Total, U.S. Total|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|86.2|Denver, CO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|87.0|Boston, MA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|87.5|San Francisco, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|88.6|Kansas City, MO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|89.8|Minneapolis, MN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|89.9|Washington, DC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2013|Both|All|93.6|Seattle, WA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|13.2|San Francisco, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|15.7|Oakland (Alameda County), CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|17.7|Minneapolis, MN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|18.3|Austin, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|18.3|Denver, CO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|18.6|Portland (Multnomah County), OR|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|19.0|Charlotte, NC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|19.3|Washington, DC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|19.4|Long Beach, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|20.4|Los Angeles, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|20.7|San Diego County, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|21.0|Boston, MA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|23.6|Houston, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|24.1|Chicago, Il|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|24.2|Dallas, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|24.2|New York City, NY|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|24.6|Columbus, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|24.9|Phoenix, AZ|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|25.1|Fort Worth (Tarrant County), TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|26.2|San Antonio, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|27.8|Kansas City, MO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|28.0|U.S. Total, U.S. Total|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|28.3|Miami (Miami-Dade County), FL|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|29.1|Indianapolis (Marion County), IN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|29.6|Las Vegas (Clark County), NV|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|31.7|Baltimore, MD|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|32.9|Cleveland, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|34.1|Detroit, MI|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2014|Both|All|34.2|Philadelphia, PA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|10.5|Seattle, WA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|13.2|San Francisco, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|16.2|Minneapolis, MN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|16.4|Oakland (Alameda County), CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|17.3|Denver, CO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|18.1|Portland (Multnomah County), OR|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|18.2|Austin, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|18.3|Washington, DC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|19.9|Charlotte, NC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|20.1|Long Beach, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|20.7|San Diego County, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|21.0|Los Angeles, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|21.2|Boston, MA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|23.7|Dallas, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|23.8|Chicago, Il|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|23.9|New York City, NY|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|24.5|Houston, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|25.1|Columbus, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|25.3|Fort Worth (Tarrant County), TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|25.7|Phoenix, AZ|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|26.9|Kansas City, MO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|27.7|San Antonio, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|27.9|U.S. Total, U.S. Total|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|28.2|Indianapolis (Marion County), IN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|28.7|Miami (Miami-Dade County), FL|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|29.1|Las Vegas (Clark County), NV|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|29.7|Baltimore, MD|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|32.2|Cleveland, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|32.8|Philadelphia, PA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2015|Both|All|33.0|Detroit, MI|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|10.5|Seattle, WA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|12.8|San Francisco, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|15.9|Oakland (Alameda County), CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|16.7|Minneapolis, MN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|17.3|Austin, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|17.6|Portland (Multnomah County), OR|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|18.0|Charlotte, NC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|18.9|Long Beach, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|19.3|Denver, CO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|19.3|Washington, DC|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|20.0|Boston, MA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|20.3|Los Angeles, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|20.3|San Diego County, CA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|23.1|Chicago, Il|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|23.4|Dallas, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|24.1|Houston, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|24.2|New York City, NY|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|25.2|Phoenix, AZ|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|25.6|Columbus, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|25.6|Fort Worth (Tarrant County), TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|26.6|Kansas City, MO|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|27.7|San Antonio, TX|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|27.7|U.S. Total, U.S. Total|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|28.2|Miami (Miami-Dade County), FL|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|28.3|Indianapolis (Marion County), IN|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|29.4|Baltimore, MD|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|30.4|Las Vegas (Clark County), NV|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|31.7|Philadelphia, PA|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|32.6|Detroit, MI|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of High School Graduates (Over Age 18)|2016|Both|All|34.8|Cleveland, OH|Percentage of the population 18 years and over who are high school graduates using US Census Bureau, American Community Survey (ACS) 1-year estimates or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID B15001 - Sex by Age by Educational Attainment for the Population 18 Years and Over||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|25.2|Fort Worth (Tarrant County), TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|25.0|50.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|26.8|San Antonio, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4865000 was used to isolate data for San Antonio, TX.|26.7|53.5||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|27.6|U.S. Total, U.S. Total|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics|||27.6|55.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|29.0|Dallas, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48113 was used to isolate data for Dallas county, TX.|28.8|57.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|29.3|Denver, CO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0820000 was used to isolate data for Denver, CO.|29.0|58.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|29.9|Kansas City, MO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2938000 was used to isolate data for Kansas City, MO.|29.6|59.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|30.4|Seattle, WA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 5363000 was used to isolate data for Seattle, WA.|30.2|60.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|31.0|Houston, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4835000 was used to isolate data for Houston, TX.|30.8|61.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|31.0|Minneapolis, MN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|30.7|61.7||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|31.6|Washington, DC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 1150000 was used to isolate data for Washington, DC.|31.3|62.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|32.9|Las Vegas (Clark County), NV|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|32.7|65.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|33.7|San Jose, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0668000 was used to isolate data for San Jose, CA.|33.5|67.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|33.8|Philadelphia, PA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|33.6|67.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|34.1|San Francisco, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0667000 was used to isolate data for San Francisco, CA.|33.9|68.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|34.2|Boston, MA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2507000 was used to isolate data for Boston, MA.|33.9|68.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|34.9|Baltimore, MD|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2404000 was used to isolate data for Baltimore, MD.|34.6|69.5||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|36.1|Chicago, Il|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|35.9|72.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|37.2|San Diego County, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 06073 was used to isolate data for San Diego County, CA.|37.1|74.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|38.7|Cleveland, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3916000 was used to isolate data for Cleveland, OH.|38.3|76.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|39.7|Oakland (Alameda County), CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0653000 was used to isolate data for Oakland, CA.|39.3|79.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|40.0|New York City, NY|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3651000 was used to isolate data for New York City, NY.|39.9|79.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|40.4|Detroit, MI|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2622000 was used to isolate data for Detroit, MI.|40.0|80.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|40.6|Long Beach, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0643000 was used to isolate data for Long Beach, CA.|40.2|80.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|43.4|Miami (Miami-Dade County), FL|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|43.2|86.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2012|Both|All|46.2|Los Angeles, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|46.1|92.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|23.6|Fort Worth (Tarrant County), TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|23.5|47.1||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|26.4|San Antonio, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4865000 was used to isolate data for San Antonio, TX.|26.2|52.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|26.6|U.S. Total, U.S. Total|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics|||26.6|53.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|27.0|Kansas City, MO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2938000 was used to isolate data for Kansas City, MO.|26.8|53.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|27.9|Phoenix, AZ|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|27.7|55.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|28.1|Seattle, WA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 5363000 was used to isolate data for Seattle, WA.|27.9|56.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|28.6|Dallas, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48113 was used to isolate data for Dallas county, TX.|28.4|57.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|29.3|Houston, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4835000 was used to isolate data for Houston, TX.|29.1|58.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|29.3|Minneapolis, MN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|29.0|58.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|29.4|Denver, CO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0820000 was used to isolate data for Denver, CO.|29.2|58.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|29.8|Washington, DC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 1150000 was used to isolate data for Washington, DC.|29.5|59.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|31.0|Las Vegas (Clark County), NV|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|30.8|61.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|31.7|San Francisco, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0667000 was used to isolate data for San Francisco, CA.|31.4|63.1||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|33.1|San Jose, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0668000 was used to isolate data for San Jose, CA.|32.9|66.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|34.5|Baltimore, MD|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2404000 was used to isolate data for Baltimore, MD.|34.2|68.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|34.5|Philadelphia, PA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|34.3|68.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|35.0|Boston, MA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2507000 was used to isolate data for Boston, MA.|34.7|69.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|35.1|Chicago, Il|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|35.0|70.1||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|35.1|Cleveland, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3916000 was used to isolate data for Cleveland, OH.|34.7|69.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|35.2|San Diego County, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 06073 was used to isolate data for San Diego County, CA.|35.0|70.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|36.7|Oakland (Alameda County), CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0653000 was used to isolate data for Oakland, CA.|36.3|73.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|37.8|Long Beach, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0643000 was used to isolate data for Long Beach, CA.|37.5|75.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|39.0|New York City, NY|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3651000 was used to isolate data for New York City, NY.|38.9|77.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|39.3|Detroit, MI|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2622000 was used to isolate data for Detroit, MI.|39.0|78.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|42.0|Miami (Miami-Dade County), FL|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|41.8|83.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2013|Both|All|44.2|Los Angeles, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|44.0|88.2||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|17.6|Columbus, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|18.4|Denver, CO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|18.7|Charlotte, NC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|19.5|Indianapolis (Marion County), IN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|19.5|Minneapolis, MN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|19.5|Washington, DC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|19.8|Kansas City, MO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|21.0|Phoenix, AZ|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|21.7|Austin, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|22.6|San Antonio, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|22.7|Seattle, WA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|23.4|U.S. Total, U.S. Total|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|24.0|Houston, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|24.7|Las Vegas (Clark County), NV|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|24.9|Dallas, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|25.4|Fort Worth (Tarrant County), TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|25.3|50.7||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|25.5|Portland (Multnomah County), OR|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|26.5|Kansas City, MO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2938000 was used to isolate data for Kansas City, MO.|26.3|52.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|26.5|U.S. Total, U.S. Total|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics|||26.4|52.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|27.4|Cleveland, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|27.5|Seattle, WA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 5363000 was used to isolate data for Seattle, WA.|27.3|54.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|27.8|San Antonio, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4865000 was used to isolate data for San Antonio, TX.|27.7|55.5||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|27.9|Phoenix, AZ|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|27.7|55.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|28.3|Denver, CO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0820000 was used to isolate data for Denver, CO.|28.1|56.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|28.9|Dallas, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48113 was used to isolate data for Dallas county, TX.|28.8|57.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|29.3|Baltimore, MD|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|29.4|Las Vegas (Clark County), NV|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|29.3|58.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|29.6|Long Beach, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|29.8|Minneapolis, MN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|29.5|59.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|30.2|San Jose, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|30.3|Washington, DC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 1150000 was used to isolate data for Washington, DC.|30.0|60.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|30.4|Houston, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4835000 was used to isolate data for Houston, TX.|30.3|60.7||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|30.6|Philadelphia, PA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|30.6|San Francisco, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0667000 was used to isolate data for San Francisco, CA.|30.4|61.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|31.1|Chicago, Il|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|31.5|San Diego County, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|32.4|San Jose, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0668000 was used to isolate data for San Jose, CA.|32.2|64.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|32.7|San Francisco, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|33.3|Oakland (Alameda County), CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|34.2|Boston, MA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2507000 was used to isolate data for Boston, MA.|33.9|68.1||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|34.7|San Diego County, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 06073 was used to isolate data for San Diego County, CA.|34.6|69.3||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|34.8|Baltimore, MD|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2404000 was used to isolate data for Baltimore, MD.|34.6|69.4||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|35.0|Chicago, Il|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|34.9|69.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|35.0|Philadelphia, PA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|34.8|69.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|35.2|Cleveland, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3916000 was used to isolate data for Cleveland, OH.|34.8|70.0||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|36.0|Oakland (Alameda County), CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0653000 was used to isolate data for Oakland, CA.|35.6|71.6||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|38.1|Long Beach, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0643000 was used to isolate data for Long Beach, CA.|37.7|75.9||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|38.5|Detroit, MI|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2622000 was used to isolate data for Detroit, MI.|38.2|76.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|38.7|Miami (Miami-Dade County), FL|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|39.1|New York City, NY|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|39.5|New York City, NY|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3651000 was used to isolate data for New York City, NY.|39.4|78.8||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|40.4|Los Angeles, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|41.4|Miami (Miami-Dade County), FL|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|41.3|82.7||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2014|Both|All|44.1|Los Angeles, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|44.0|88.1||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|16.8|Kansas City, MO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|17.4|Columbus, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|18.0|Fort Worth (Tarrant County), TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|18.8|Indianapolis (Marion County), IN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|19.8|Denver, CO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|19.8|Seattle, WA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|20.2|Minneapolis, MN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|20.2|San Antonio, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|20.6|Washington, DC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|21.8|Austin, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|22.1|Houston, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|22.3|U.S. Total, U.S. Total|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|22.8|Phoenix, AZ|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|22.9|Las Vegas (Clark County), NV|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|24.9|Dallas, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|25.1|Portland (Multnomah County), OR|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|26.1|San Jose, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|26.6|Baltimore, MD|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|27.9|Cleveland, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|28.3|Philadelphia, PA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|29.6|Chicago, Il|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|30.2|Boston, MA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|30.3|San Francisco, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|31.7|San Diego County, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|32.9|Oakland (Alameda County), CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|34.9|Long Beach, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|38.0|New York City, NY|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|38.4|Miami (Miami-Dade County), FL|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2015|Both|All|40.2|Los Angeles, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|15.1|Columbus, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|17.2|Indianapolis (Marion County), IN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|17.8|Charlotte, NC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|18.0|Kansas City, MO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|18.3|Fort Worth (Tarrant County), TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|19.3|Minneapolis, MN|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|19.7|Seattle, WA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|20.0|Austin, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|20.2|Washington, DC|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|20.5|San Antonio, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|20.7|Portland (Multnomah County), OR|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|21.3|Dallas, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|21.3|Denver, CO|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|21.3|U.S. Total, U.S. Total|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|22.3|Phoenix, AZ|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|23.1|Houston, TX|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|24.8|Las Vegas (Clark County), NV|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|25.1|Baltimore, MD|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|25.5|San Jose, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|26.1|Cleveland, OH|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|26.6|Boston, MA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|27.8|Chicago, Il|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|27.8|Philadelphia, PA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|28.9|Oakland (Alameda County), CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|29.1|Detroit, MI|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|29.3|Long Beach, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|30.4|San Francisco, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|31.2|San Diego County, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|35.5|Miami (Miami-Dade County), FL|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|36.5|New York City, NY|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Households Whose Housing Costs Exceed 35% of Income|2016|Both|All|38.6|Los Angeles, CA|Proportion of households whose gross housing costs are 35% or more of their income using US Census Bureau, American Community Survey 1-year estimates, as collected through American FactFinder. Calculation=[SMOCAPI with mortgage + SMOCAPI without mortgage + GRAPI)] / Number of Occupied Housing Units. SMOCAPI and GRAPI estimates include 35.0 percent or more.|US Census Bureau, American Community Survey 1-year estimates; Table ID DP04 - Selected Housing Characteristics||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|10.5|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|9.8|11.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|11.0|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|10.1|11.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|12.2|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|11.1|13.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|12.3|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|11.5|13.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|13.8|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|13.5|14.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|14.0|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|13.4|14.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|14.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|13.2|14.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|14.8|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||14.7|14.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|16.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|15.7|17.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|16.7|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|15.6|17.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|16.9|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|15.6|18.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|17.0|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|16.4|17.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|17.1|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|15.9|18.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|19.2|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|18.6|19.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|19.6|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|18.6|20.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|20.3|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|19.0|21.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|21.1|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|20.3|21.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|22.5|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|21.7|23.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|22.9|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|22.1|23.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|22.9|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|22.0|23.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|25.1|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|24.6|25.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|26.7|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|26.0|27.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|28.5|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|27.7|29.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|All|29.1|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|28.4|29.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|9.3|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|3.7|14.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|11.3|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|4.5|18.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|16.8|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.0|24.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|17.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|12.5|22.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|19.2|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.4|28.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|20.7|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|11.2|30.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|22.6|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|11.2|34.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|24.0|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|17.9|30.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|25.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.6|38.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|25.2|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|12.2|38.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|25.6|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|14.5|36.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|26.4|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|18.7|34.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|27.0|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|19.9|34.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|27.3|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|18.5|36.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|27.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||26.9|27.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|31.2|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|16.4|46.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|33.2|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|27.1|39.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|34.5|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|24.2|44.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|36.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|22.1|50.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|American Indian/Alaska Native|41.7|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|21.0|62.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|3.7|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|2.3|5.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|8.4|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|4.3|12.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|10.2|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.0|14.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|10.9|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|9.3|12.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|10.9|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|9.8|12.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|11.9|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|10.5|13.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|13.7|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|11.1|16.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|14.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.5|16.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|14.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|11.7|17.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|14.6|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.|8.4|20.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|15.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone|14.8|15.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|15.3|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|9.6|21.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|15.8|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|7.2|24.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|16.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|11.6|22.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|17.2|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|16.3|18.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|17.6|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|15.4|19.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|18.1|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|15.8|20.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|18.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|15.4|21.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|19.5|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|15.8|23.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|21.9|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|18.2|25.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|21.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|14.5|29.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|22.1|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|20.8|23.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|23.5|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|20.6|26.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|23.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|21.0|26.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|24.3|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|9.5|39.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Asian/PI|27.0|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|20.6|33.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|6.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|6.0|7.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|7.2|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|5.6|8.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|10.5|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.3|13.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|12.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|11.5|13.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|12.9|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|12.5|13.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|13.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|9.2|16.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|13.3|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|11.4|15.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|14.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|11.0|17.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|14.6|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|11.6|17.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|15.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|14.2|16.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|17.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|14.3|20.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|17.3|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||17.2|17.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|17.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|16.2|19.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|18.4|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|15.8|21.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|18.5|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|17.5|19.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|19.3|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|18.5|20.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|19.5|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|18.3|20.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|20.3|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|17.3|23.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|21.4|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|20.2|22.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|22.8|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|21.6|24.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|22.8|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|20.5|25.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|23.6|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|21.2|26.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|24.1|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|21.9|26.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Black|29.6|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|28.1|31.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|6.8|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|5.6|8.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|12.0|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|9.6|14.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|18.6|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|15.3|21.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|20.1|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|19.4|20.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|20.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|17.8|22.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|21.2|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|18.6|23.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|21.8|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|19.9|23.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|24.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|23.8|25.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|28.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|27.1|29.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|28.8|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|26.5|31.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|28.9|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|27.7|30.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|29.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||28.8|29.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|29.1|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|25.0|33.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|29.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|24.7|33.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|29.5|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|27.0|32.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|29.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|24.1|34.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|30.6|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|27.6|33.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|33.3|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|32.4|34.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|34.3|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|33.6|35.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|35.4|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|33.7|37.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|35.7|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|34.4|37.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|36.1|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|29.4|42.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|36.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|30.9|41.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|39.6|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|37.7|41.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|41.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|40.2|43.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Hispanic|42.8|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|41.4|44.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|3.6|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 1150000 was used to isolate data for Washington, DC.|0.8|6.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|8.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|4.9|12.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|8.9|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|6.0|11.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|11.0|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|8.6|13.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|11.3|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.8|13.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|12.9|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|7.6|18.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|13.1|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|11.2|15.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|13.3|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.7|14.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|13.3|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|9.5|17.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|13.4|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.2|18.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|14.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||13.8|14.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|14.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|7.9|21.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|15.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|11.6|18.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|15.3|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|12.2|18.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|15.8|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|11.8|19.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|15.9|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|10.7|21.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|16.6|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|10.8|22.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|16.8|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|12.1|21.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|16.9|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|13.0|20.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|17.3|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|13.8|20.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|19.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|17.6|22.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|20.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|16.3|24.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|20.7|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|16.9|24.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|21.8|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|17.0|26.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|25.2|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|14.9|35.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Multiracial|25.8|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|20.0|31.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Other|5.7|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0667000 was used to isolate data for San Francisco, CA.|0.9|12.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Other|12.6|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|2.3|22.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Other|18.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander|16.6|19.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Other|19.5|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 06073 was used to isolate data for San Diego County, CA.|12.0|27.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Other|22.7|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|13.4|32.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|Other|25.5|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0668000 was used to isolate data for San Jose, CA.|15.2|35.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|2.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|2.3|3.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|3.1|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|2.4|3.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|6.4|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.6|7.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|7.3|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.4|8.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|7.3|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.5|8.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|7.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|7.2|7.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|8.2|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|7.2|9.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|8.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|7.3|9.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|8.5|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|6.8|10.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|9.6|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|8.8|10.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|10.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|8.9|11.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|10.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||10.3|10.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|10.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|10.0|11.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|10.7|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.1|11.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|11.0|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|10.1|11.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|11.2|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|9.9|12.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|11.8|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.9|13.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|13.0|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|12.2|13.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|13.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|12.3|13.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|13.1|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|11.7|14.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|13.2|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|12.4|14.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|13.2|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|11.9|14.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|13.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|12.5|14.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|14.6|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|13.1|16.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|15.7|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|14.7|16.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Both|White|19.7|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|17.0|22.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|3.6|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|2.9|4.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|4.8|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|4.3|5.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|9.4|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|8.5|10.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|9.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|8.6|10.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|9.9|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.7|11.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|11.1|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|10.4|11.8||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|11.6|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.3|11.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|12.4|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|11.4|13.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|13.4|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|12.2|14.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|13.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||13.3|13.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|13.9|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|12.6|15.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|14.5|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|13.4|15.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|15.5|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|13.9|17.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|15.8|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|14.6|17.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|15.9|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|15.2|16.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|16.7|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|16.1|17.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|19.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|17.7|20.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|20.2|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|19.3|21.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|20.5|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|19.5|21.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|21.1|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|20.3|21.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|21.5|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|20.6|22.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|22.5|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|21.9|23.1||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|24.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|23.6|25.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|26.8|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|25.9|27.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Female|All|27.3|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|26.6|28.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|6.6|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|5.7|7.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|7.2|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|6.2|8.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|11.6|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|10.6|12.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|12.7|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|11.5|13.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|14.5|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|13.1|15.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|15.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|14.3|16.7||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|15.5|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|14.5|16.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|16.2|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|15.8|16.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|16.2|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||16.1|16.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|17.3|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|16.3|18.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|18.1|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|17.3|18.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|18.8|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|17.2|20.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|18.9|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|17.6|20.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|20.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|18.5|21.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|20.3|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|19.0|21.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|21.5|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|20.0|23.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|21.8|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|21.0|22.6||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|22.0|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|21.1|22.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|23.7|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|22.4|25.0||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|23.9|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|22.9|24.9||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|24.3|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|23.4|25.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|25.5|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|24.5|26.5||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|27.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|27.2|28.4||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|29.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|28.4|30.2||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|30.3|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|29.3|31.3||
Social and Economic Factors|Percent of Population Uninsured|2012|Male|All|31.0|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|30.1|31.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|6.7|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|6.1|7.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|9.2|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|8.5|9.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|10.7|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|10.0|11.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|13.0|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|11.6|14.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|13.3|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|12.4|14.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|13.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|13.1|13.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|14.5|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||14.4|14.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|14.9|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|14.3|15.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|15.7|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|14.8|16.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|16.2|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|15.3|17.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|16.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|15.7|16.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|17.0|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|15.9|18.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|17.5|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|16.1|18.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|19.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|17.8|20.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|19.4|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|18.3|20.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|19.7|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|19.1|20.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|20.7|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|20.0|21.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|20.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|20.0|21.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|21.6|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|20.9|22.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|23.6|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|22.6|24.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|24.2|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|23.8|24.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|27.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|26.5|27.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|28.4|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|27.8|29.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|All|29.1|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|28.5|29.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|9.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|0.0|20.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|10.3|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|2.7|17.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|11.2|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|3.1|19.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|16.4|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|7.2|25.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|20.1|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|14.6|25.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|21.8|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|15.0|28.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|26.9|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||26.4|27.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|28.0|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|16.0|40.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|29.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|14.0|44.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|30.5|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|25.6|35.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|32.4|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|16.9|47.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|33.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|25.6|41.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|34.7|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|22.8|46.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|36.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|27.1|45.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|36.3|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|15.2|57.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|36.4|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|28.4|44.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|37.0|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|21.6|52.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|37.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|26.7|47.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|41.1|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|24.4|57.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|American Indian/Alaska Native|41.7|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|33.7|49.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|2.7|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|1.7|3.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|8.5|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|4.2|12.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|9.7|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|8.3|11.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|10.6|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|9.6|11.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|10.7|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.7|13.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|11.5|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|5.7|17.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|12.6|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|9.4|15.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|13.1|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|8.4|17.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|14.6|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone|14.3|14.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|14.8|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|12.1|17.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|14.9|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|9.2|20.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|15.7|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|14.9|16.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|16.1|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|12.2|20.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|17.9|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.|9.3|26.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|18.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|16.0|20.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|19.9|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|12.3|27.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|20.4|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|19.1|21.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|21.1|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|18.7|23.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|21.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|18.4|24.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|22.1|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|19.3|24.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|22.3|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|16.6|28.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|25.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|22.0|28.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|26.6|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|18.6|34.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Asian/PI|27.1|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|22.2|32.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|5.0|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|4.0|6.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|7.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|7.1|8.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|10.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|9.5|11.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|10.5|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.8|13.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|11.6|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|8.3|14.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|13.0|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|10.3|15.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|14.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|12.1|16.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|14.6|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.3|17.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|15.5|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|14.2|16.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|15.8|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|14.8|16.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|17.4|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|15.1|19.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|18.5|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|14.5|22.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|18.9|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|17.7|20.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|19.3|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|17.9|20.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|19.4|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|18.6|20.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|19.5|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|16.8|22.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|19.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|13.9|25.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|20.1|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|18.3|21.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|21.8|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|19.6|24.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|21.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|19.6|24.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|23.3|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|22.0|24.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Black|29.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|28.3|31.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|7.6|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|5.7|9.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|10.4|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|7.6|13.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|19.1|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|16.0|22.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|19.6|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|19.0|20.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|20.5|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|18.0|23.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|21.2|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|19.2|23.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|25.1|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|24.1|26.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|25.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|20.3|30.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|26.5|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|24.2|28.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|26.8|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|21.8|31.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|26.9|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|24.0|29.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|27.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|25.0|29.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|27.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|26.0|28.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|28.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|24.7|31.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|28.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||28.2|28.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|30.0|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|28.8|31.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|32.5|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|31.7|33.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|32.6|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|30.9|34.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|33.0|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|32.3|33.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|34.7|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|29.6|39.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|35.4|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|33.6|37.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|36.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|34.5|38.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|41.9|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|34.4|49.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|42.1|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|40.9|43.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Hispanic|42.9|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|41.6|44.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|7.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|3.8|11.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|8.1|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|4.6|11.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|8.1|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|4.8|11.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|8.5|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.1|11.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|10.6|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|3.4|17.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|10.7|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|6.8|14.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|12.0|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|10.5|13.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|12.5|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|7.2|17.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|12.6|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.3|13.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|12.7|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|8.3|17.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|13.2|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|10.5|15.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|13.2|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||13.0|13.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|13.3|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.8|17.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|13.6|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|10.2|17.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|13.7|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.8|16.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|13.9|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.9|17.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|15.0|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|9.8|20.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|16.1|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|13.5|18.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|16.3|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|13.2|19.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|17.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|12.0|23.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|17.9|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|14.5|21.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|18.5|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|16.3|20.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|19.7|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|15.9|23.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|21.2|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|17.3|25.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Multiracial|27.8|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|21.4|34.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|10.2|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0667000 was used to isolate data for San Francisco, CA.|0.4|20.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|12.9|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|5.0|20.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|17.9|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander|16.6|19.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|20.5|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 06073 was used to isolate data for San Diego County, CA.|12.5|28.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|29.7|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|19.9|39.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|42.2|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|24.5|59.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|Other|47.8|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 48113 was used to isolate data for Dallas county, TX.|21.1|74.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|2.8|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|2.2|3.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|3.5|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|2.7|4.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|6.6|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.7|7.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|7.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.3|7.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|7.7|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|6.7|8.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|7.9|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|7.5|8.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|8.8|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|7.3|10.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|8.8|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|7.8|9.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|9.4|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|7.9|10.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|10.0|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.1|10.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|10.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|9.1|11.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|10.1|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|9.4|10.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|10.6|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|9.9|11.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|10.8|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.4|12.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|10.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|9.8|12.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|11.4|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|10.1|12.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|11.6|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|10.6|12.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|11.9|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|11.1|12.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|12.8|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|12.0|13.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|12.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|12.2|13.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|14.0|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|13.0|15.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|14.7|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|13.7|15.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|15.5|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|14.7|16.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|16.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|14.8|18.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Both|White|19.0|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|16.3|21.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|3.3|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|2.7|3.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|4.7|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|4.1|5.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|8.1|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.2|9.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|9.1|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|8.1|10.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|10.1|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|9.1|11.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|10.9|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|10.6|11.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|11.7|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|10.0|13.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|12.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|11.0|13.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|13.3|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.5|14.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|13.3|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||13.2|13.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|13.5|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|12.3|14.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|13.9|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|12.8|15.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|14.3|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|12.8|15.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|15.0|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|14.4|15.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|15.8|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|14.4|17.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|15.9|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|14.6|17.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|16.7|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|16.1|17.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|18.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|16.7|19.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|19.3|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|18.3|20.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|19.5|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|18.8|20.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|20.6|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|19.8|21.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|21.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|19.9|22.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|21.7|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|21.1|22.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|24.8|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|24.0|25.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|25.9|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|25.1|26.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Female|All|27.4|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|26.8|28.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|5.5|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|4.6|6.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|8.8|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|7.9|9.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|12.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|11.5|13.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|12.9|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|11.7|14.1||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|14.2|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|12.7|15.7||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|14.5|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|13.4|15.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|15.8|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||15.7|15.9||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|16.2|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|15.8|16.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|16.7|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|15.9|17.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|17.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|16.9|18.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|17.9|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|16.8|19.0||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|18.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|17.0|19.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|20.1|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|18.4|21.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|20.8|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|19.0|22.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|21.9|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|21.0|22.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|22.5|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|21.6|23.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|22.6|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|21.7|23.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|22.8|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|22.1|23.5||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|23.3|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|22.0|24.6||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|26.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|25.0|27.2||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|26.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|26.3|27.3||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|29.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|28.8|30.4||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|30.8|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|29.8|31.8||
Social and Economic Factors|Percent of Population Uninsured|2013|Male|All|30.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|30.1|31.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|5.1|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|4.5|5.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|5.3|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|4.6|6.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|6.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.9|7.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|7.2|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.7|7.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|8.1|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|7.5|8.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|8.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|7.8|9.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|9.3|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|8.3|10.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|10.8|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|9.7|11.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|11.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.1|11.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|11.7|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||11.6|11.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|11.8|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|10.9|12.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|12.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|11.8|12.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|12.4|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|11.4|13.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|12.6|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.0|13.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|14.3|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|13.8|14.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|14.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|13.0|15.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|14.8|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|13.6|16.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|15.2|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|14.4|16.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|16.0|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|15.3|16.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|17.6|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|17.2|18.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|17.8|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|17.0|18.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|18.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|17.4|18.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|22.1|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|21.5|22.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|22.9|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|22.2|23.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|All|25.6|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|24.9|26.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|6.8|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|0.8|12.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|8.9|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|3.5|14.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|10.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.8|15.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|11.8|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|1.5|22.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|12.3|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|4.7|19.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|14.4|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|1.5|27.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|15.1|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|8.4|21.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|15.6|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|7.0|24.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|16.2|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|8.6|23.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|16.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.9|20.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|17.4|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.5|24.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|17.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|12.7|22.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|20.2|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.5|29.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|21.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|4.5|38.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|21.4|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|8.0|34.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|22.7|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|14.1|31.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|23.1|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||22.7|23.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|27.9|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|17.0|38.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|29.0|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|22.2|35.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|29.3|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|11.5|47.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|American Indian/Alaska Native|29.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|23.8|35.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|4.5|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|3.0|6.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|5.0|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|2.6|7.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|5.4|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|4.6|6.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|6.4|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.4|7.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|6.9|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|5.0|8.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|9.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|8.1|10.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|9.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|4.9|14.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|9.7|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|7.2|12.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|10.5|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|7.1|13.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|10.6|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone|10.4|10.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|10.8|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.4|14.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|11.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.0|13.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|11.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|7.1|16.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|12.8|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.3|14.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|12.8|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|12.2|13.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|12.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|9.1|16.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|13.0|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|11.1|14.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|13.6|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.|6.2|21.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|14.1|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|13.1|15.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|14.5|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|11.9|17.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|15.0|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|11.4|18.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|15.4|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|12.1|18.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|16.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|7.9|24.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|17.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|15.1|19.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|20.0|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|7.5|32.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Asian/PI|20.2|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|17.7|22.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|5.0|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|4.2|5.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|6.0|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|4.7|7.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|6.5|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|3.8|9.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|8.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|7.6|9.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|8.5|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|7.0|10.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|9.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|6.8|11.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|9.2|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|7.6|10.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|9.4|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.5|12.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|9.4|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.0|12.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|9.5|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|6.9|12.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|10.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|10.0|11.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|11.9|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|10.7|13.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|12.8|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|10.1|15.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|12.9|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.0|13.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|13.0|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|12.3|13.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|13.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|10.5|15.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|13.6|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||13.5|13.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|13.7|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|10.9|16.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|14.0|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|13.2|14.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|14.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|12.4|16.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|17.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|16.2|18.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|17.4|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|15.4|19.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|19.4|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|18.3|20.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|19.4|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|16.5|22.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Black|24.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|23.5|26.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|13.6|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|10.6|16.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|13.9|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|11.4|16.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|14.3|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|12.9|15.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|16.3|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|12.8|19.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|17.0|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|16.3|17.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|17.7|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|12.8|22.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|18.8|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|16.1|21.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|20.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|18.0|22.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|21.2|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|18.9|23.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|21.7|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|20.7|22.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|21.9|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|20.8|23.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|22.9|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|21.7|24.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|23.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|20.7|25.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|23.5|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||23.3|23.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|24.5|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|23.7|25.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|25.1|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|24.5|25.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|26.3|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|24.8|27.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|26.4|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|19.2|33.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|26.8|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|22.5|31.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|28.5|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|27.1|29.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|31.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|29.7|32.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|31.5|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|25.3|37.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|32.0|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|26.0|38.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|36.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|35.2|37.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Hispanic|39.1|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|37.9|40.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|3.3|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|1.1|5.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|5.1|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|2.2|8.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|5.3|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|3.7|6.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|5.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|2.0|9.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|6.2|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|3.2|9.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|7.3|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|4.1|10.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|7.6|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|3.9|11.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|8.4|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.4|11.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|8.7|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.8|11.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|9.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|8.3|10.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|9.9|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|6.3|13.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|9.9|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|7.8|12.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|9.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|5.8|14.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|10.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||10.2|10.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|10.7|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|7.1|14.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|10.7|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|8.3|13.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|10.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|9.3|12.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|10.9|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|6.6|15.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|11.0|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.8|14.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|11.9|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|9.2|14.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|12.4|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|9.8|15.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|13.0|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|10.2|15.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|14.3|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.4|19.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|16.8|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|12.9|20.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|17.4|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|13.1|21.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Multiracial|23.1|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|18.8|27.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|10.6|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0643000 was used to isolate data for Long Beach, CA.|5.3|15.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|13.5|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander|12.4|14.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|14.1|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.5|19.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|14.2|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.5|21.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|15.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.5|21.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|17.6|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|0.5|34.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|18.8|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.5|30.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|Other|23.2|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 3651000 was used to isolate data for New York City, NY.|6.4|40.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|1.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|1.4|2.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|3.2|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|2.2|4.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|4.6|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|3.9|5.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|4.7|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|3.9|5.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|4.9|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.3|5.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|5.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.4|5.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|6.1|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|5.0|7.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|6.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|6.1|6.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|6.5|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|4.7|8.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|6.6|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|5.4|7.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|8.1|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||8.0|8.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|8.2|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|7.7|8.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|8.3|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|7.8|8.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|8.4|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|7.2|9.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|8.5|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.4|9.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|8.6|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|7.6|9.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|9.0|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|8.1|9.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|9.1|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|8.3|9.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|9.6|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.8|10.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|10.5|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|9.8|11.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|10.8|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|9.2|12.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|10.8|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|9.9|11.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|11.2|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|10.0|12.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|11.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|10.7|12.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Both|White|16.9|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|14.2|19.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|3.8|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|3.1|4.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|5.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.7|6.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|6.3|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.6|7.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|7.1|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|6.3|7.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|7.3|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|6.6|8.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|8.4|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.2|9.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|8.4|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|7.2|9.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|9.0|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|7.9|10.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|9.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|9.2|9.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|10.1|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.4|10.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|10.4|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|9.3|11.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|10.5|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||10.4|10.6||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|11.4|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|10.9|11.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|11.8|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|10.8|12.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|11.9|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.4|12.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|13.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.9|14.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|13.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|12.5|15.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|14.6|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|13.9|15.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|15.3|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|14.9|15.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|16.2|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|15.3|17.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|16.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|15.5|17.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|16.7|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|15.7|17.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|20.6|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|19.9|21.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|21.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|20.5|22.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Female|All|24.0|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|23.1|24.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|6.7|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|5.9|7.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|6.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|5.9|7.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|7.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.7|8.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|8.0|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.3|8.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|9.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|8.3|9.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|10.0|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|9.0|11.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|12.9|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.|||12.8|13.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|13.2|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|12.6|13.8||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|13.4|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|11.9|14.9||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|13.6|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|13.2|14.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|14.8|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|13.5|16.1||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|14.8|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|13.2|16.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|15.5|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|14.7|16.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|16.5|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|15.0|18.0||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|16.9|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|16.3|17.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|17.4|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|16.6|18.2||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|18.6|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|17.5|19.7||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|19.1|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|17.9|20.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|19.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|18.3|20.3||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|20.0|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|19.5|20.5||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|23.7|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|23.0|24.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|24.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|23.8|25.4||
Social and Economic Factors|Percent of Population Uninsured|2014|Male|All|27.2|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|26.3|28.1||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|3.8|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|4.4|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|4.8|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|6.1|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|7.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|7.9|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|8.4|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|8.5|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|8.8|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|8.8|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|9.3|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|9.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|9.7|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|9.8|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|10.1|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|10.5|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|11.3|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|12.1|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|12.1|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|12.1|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|13.3|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|13.4|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|14.4|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|14.8|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|16.0|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|16.3|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|18.3|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|20.7|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|All|22.3|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|1.5|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|3.1|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|3.9|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|4.7|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|5.0|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|5.2|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|5.3|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|6.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|6.9|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|7.1|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|7.8|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|8.6|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|8.7|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|9.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.0|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.4|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.5|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.5|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.6|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|10.8|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|12.5|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|12.6|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|12.6|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|12.9|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|14.1|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|14.3|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|15.5|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|16.3|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Asian/PI|22.8|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|3.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|3.5|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|3.6|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|4.4|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|4.4|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|6.3|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|6.3|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|7.4|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|7.5|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|7.8|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|7.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|8.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|8.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|8.7|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|8.8|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|9.1|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|9.5|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|10.0|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|10.7|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|10.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|11.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|11.3|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|12.1|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|12.4|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|13.4|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|14.1|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|15.3|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|15.7|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|16.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|17.2|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Black|21.3|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|4.8|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|9.1|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|11.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|11.1|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|12.4|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|13.5|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|14.3|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|15.3|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|16.0|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|16.1|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|16.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|17.2|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|18.5|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|19.1|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|19.5|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|19.5|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|19.7|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|22.9|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|24.1|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|24.8|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|26.9|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|26.9|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|27.2|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|28.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|29.4|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|30.6|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|31.0|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|33.0|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|34.9|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Hispanic|36.0|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|2.9|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|3.4|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|3.6|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Native Hawaiian or other Pacific Islander; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|4.0|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|4.1|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|4.2|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|5.1|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|5.2|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|5.2|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|5.3|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|5.9|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|6.3|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|6.5|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|6.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|7.1|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|7.2|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|7.2|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|7.7|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|8.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|8.3|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|8.5|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|9.0|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|9.9|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|10.0|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|10.8|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|11.2|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|11.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|11.7|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|13.2|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|Multiracial|21.3|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|2.3|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|3.1|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|3.4|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|4.0|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|5.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|5.9|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|6.1|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|6.8|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|7.1|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|7.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|7.4|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|8.2|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|8.4|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|8.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|9.0|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|9.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|10.5|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|10.7|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|11.1|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|11.2|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|11.6|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|12.4|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|13.2|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|13.6|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|14.2|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|15.2|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|16.7|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|17.5|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Both|White|22.9|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|2.6|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|3.7|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|4.2|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|5.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|6.0|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|6.0|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|6.0|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|6.7|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|6.9|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|7.0|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|7.5|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|7.7|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|7.7|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|7.8|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|8.3|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|8.8|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|9.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|10.0|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|10.4|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|10.6|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|11.4|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|12.7|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|13.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|15.2|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|15.5|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|16.8|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|19.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Female|All|20.7|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|4.8|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|5.1|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|5.1|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|5.3|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|7.1|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|8.0|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|9.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|9.4|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|9.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|9.9|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|10.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|10.4|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|10.5|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|11.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|11.8|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|11.8|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|12.4|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|12.9|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|13.3|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|13.7|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|14.0|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|14.4|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|14.4|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|15.6|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|16.1|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|16.4|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|16.6|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|17.5|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|19.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|22.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2015|Male|All|24.0|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|3.1|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|3.3|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|3.9|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|3.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|5.0|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|5.8|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|6.1|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|6.8|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|7.1|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|7.4|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|7.5|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|7.8|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|8.2|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|8.6|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|8.6|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|8.7|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|9.0|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|9.6|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|11.0|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|11.2|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|11.3|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|12.1|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|13.1|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|13.7|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|15.6|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|16.1|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|16.8|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|19.6|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|All|22.2|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|4.8|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|5.2|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|9.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|9.6|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|11.6|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|12.3|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|12.3|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|13.2|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|15.4|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|16.5|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|16.5|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|17.3|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|19.2|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|19.4|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|20.0|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|21.6|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|21.8|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|23.2|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|25.8|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|28.8|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|American Indian/Alaska Native|28.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|2.7|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|3.0|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|3.9|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|4.3|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|4.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|5.0|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|5.1|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|5.4|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|5.5|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|5.8|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|6.0|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|6.8|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|7.5|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|7.5|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|7.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|8.1|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|8.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|8.2|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|8.6|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|8.9|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|9.0|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|10.2|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|10.3|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|10.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|10.3|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|11.3|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|11.9|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|11.9|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|12.8|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|15.6|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Asian/PI|17.0|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|3.1|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|4.3|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|5.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|5.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|5.8|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|6.0|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|6.3|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|6.5|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|6.7|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|6.8|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.0|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.0|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.2|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.6|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.6|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.7|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|7.8|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|8.1|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|8.1|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|9.0|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|9.7|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|9.8|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Asian alone||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|10.6|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|10.9|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|11.7|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|12.3|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|14.1|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|14.1|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|14.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|16.5|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Black|18.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|4.2|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|6.4|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|6.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|8.5|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|9.2|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|11.1|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|11.9|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|12.5|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|13.0|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|14.6|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|15.2|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|16.7|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|16.9|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|17.3|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|17.4|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|18.0|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|18.0|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|18.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|19.3|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|19.6|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|22.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|22.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|24.3|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|24.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|25.7|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|25.9|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|29.1|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|29.3|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|32.0|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|32.2|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Hispanic|34.5|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|2.2|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|2.5|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|3.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|3.2|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|3.6|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|3.9|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|4.7|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|4.8|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|5.9|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.0|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.2|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.5|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.7|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.7|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.8|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|6.9|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|7.0|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|7.3|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|7.5|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|8.1|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|8.2|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|8.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|9.3|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|11.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|12.1|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|12.4|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|12.8|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|13.8|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|14.1|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|14.8|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|Multiracial|14.9|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|1.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|2.3|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|2.3|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|2.4|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|3.4|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|3.7|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|4.8|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|5.8|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|5.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|5.9|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|6.9|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|7.5|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|7.7|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|8.0|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|8.0|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|8.2|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|8.5|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|8.8|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|8.9|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|9.1|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|9.2|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|9.8|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|11.0|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|11.3|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|12.1|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|12.1|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|14.7|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|15.7|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|15.9|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|21.4|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Both|White|22.2|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|2.3|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|2.9|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|3.0|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|3.1|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|4.7|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|4.7|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|4.9|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|5.4|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|5.6|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|5.9|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|5.9|Oakland (Alameda County), CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|6.1|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|6.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|6.5|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|7.2|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|7.3|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|7.6|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|7.8|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|8.0|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|9.5|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|9.6|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|9.7|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|11.2|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|11.8|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|12.5|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|14.7|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|14.8|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|15.6|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|18.3|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Female|All|21.0|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|3.6|San Francisco, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|3.9|Boston, MA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2507000 was used to isolate data for Boston, MA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|4.8|Seattle, WA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|5.1|Washington, DC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 1150000 was used to isolate data for Washington, DC.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|5.4|San Jose, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|6.7|Portland (Multnomah County), OR|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 41051 was used to isolate data for Portland (Multnomah County), OR.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|6.8|Minneapolis, MN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|8.3|Baltimore, MD|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|8.4|Columbus, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|8.6|San Diego County, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|9.4|New York City, NY|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3651000 was used to isolate data for New York City, NY.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|9.6|U.S. Total, U.S. Total|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|9.9|Long Beach, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|10.3|Cleveland, OH|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|10.4|Detroit, MI|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|10.4|Philadelphia, PA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|10.8|Indianapolis (Marion County), IN|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|11.1|Denver, CO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0820000 was used to isolate data for Denver, CO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|11.2|Chicago, Il|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|12.5|Charlotte, NC|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|12.7|Kansas City, MO|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|13.1|Las Vegas (Clark County), NV|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|13.3|Los Angeles, CA|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|13.6|Austin, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|15.7|Phoenix, AZ|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|16.5|Fort Worth (Tarrant County), TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|17.5|San Antonio, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|18.0|Miami (Miami-Dade County), FL|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|21.0|Dallas, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.||||
Social and Economic Factors|Percent of Population Uninsured|2016|Male|All|23.4|Houston, TX|Percent uninsured using US Census Bureau, American Community Survey 1-year estimates (includes civilian noninstitutionalized population), as collected through American FactFinder, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2701 - Health Insurance Coverage Status.||FIPS code 4835000 was used to isolate data for Houston, TX.||||
Social and Economic Factors|Percent Unemployed|2012|Both|All|5.9|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.2|6.6||
Social and Economic Factors|Percent Unemployed|2012|Both|All|7.2|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|6.4|8.0||
Social and Economic Factors|Percent Unemployed|2012|Both|All|7.6|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|7.1|8.1||
Social and Economic Factors|Percent Unemployed|2012|Both|All|7.7|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.0|8.4||
Social and Economic Factors|Percent Unemployed|2012|Both|All|8.6|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|8.2|9.0||
Social and Economic Factors|Percent Unemployed|2012|Both|All|8.7|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.7|9.7||
Social and Economic Factors|Percent Unemployed|2012|Both|All|8.9|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|8.1|9.7||
Social and Economic Factors|Percent Unemployed|2012|Both|All|9.4|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||9.3|9.5||
Social and Economic Factors|Percent Unemployed|2012|Both|All|9.6|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|8.8|10.4||
Social and Economic Factors|Percent Unemployed|2012|Both|All|9.6|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|9.2|10.0||
Social and Economic Factors|Percent Unemployed|2012|Both|All|9.7|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|9.1|10.3||
Social and Economic Factors|Percent Unemployed|2012|Both|All|9.8|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|9.0|10.6||
Social and Economic Factors|Percent Unemployed|2012|Both|All|9.8|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|9.1|10.5||
Social and Economic Factors|Percent Unemployed|2012|Both|All|10.3|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|9.3|11.3||
Social and Economic Factors|Percent Unemployed|2012|Both|All|10.6|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|10.4|10.8||
Social and Economic Factors|Percent Unemployed|2012|Both|All|11.9|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|11.3|12.5||
Social and Economic Factors|Percent Unemployed|2012|Both|All|12.7|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|12.2|13.2||
Social and Economic Factors|Percent Unemployed|2012|Both|All|13.0|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|11.9|14.1||
Social and Economic Factors|Percent Unemployed|2012|Both|All|13.7|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|13.3|14.1||
Social and Economic Factors|Percent Unemployed|2012|Both|All|15.5|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|14.4|16.6||
Social and Economic Factors|Percent Unemployed|2012|Both|All|15.9|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|15.1|16.7||
Social and Economic Factors|Percent Unemployed|2012|Both|All|19.4|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|17.9|20.9||
Social and Economic Factors|Percent Unemployed|2012|Both|All|27.7|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|26.5|28.9||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|12.0|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|5.9|18.1||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|13.0|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|5.9|20.1||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|15.9|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||15.4|16.4||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|19.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.8|27.2||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|19.6|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|12.4|26.8||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|20.1|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|12.4|27.8||
Social and Economic Factors|Percent Unemployed|2012|Both|American Indian/Alaska Native|23.2|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|17.6|28.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|1.7|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|0.5|2.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|4.3|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|2.9|5.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|4.9|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|1.6|8.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|5.0|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|0.7|9.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|6.0|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|2.3|9.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|6.4|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.1|7.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|6.8|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|5.0|8.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|7.1|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone|6.9|7.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|7.2|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|5.4|9.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|7.3|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|5.2|9.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|7.3|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.2|8.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|7.8|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.5|9.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|8.2|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|5.6|10.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|8.4|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|7.8|9.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|8.7|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|7.7|9.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|9.8|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.1|11.5||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|9.8|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|4.5|15.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|9.8|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.2|13.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|10.8|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|8.3|13.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Asian/PI|11.3|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|8.4|14.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|12.7|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|8.6|16.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|13.4|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|12.2|14.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|13.6|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|10.4|16.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|13.6|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|10.4|16.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|13.7|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|11.8|15.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|14.0|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|11.2|16.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|14.9|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|9.7|20.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|15.0|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|12.9|17.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|15.7|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|15.1|16.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|15.9|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|13.6|18.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|16.4|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|12.6|20.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|16.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||16.6|17.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|18.1|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|16.4|19.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|18.1|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|14.8|21.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|18.3|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|16.9|19.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|18.6|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|16.0|21.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|19.5|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|15.9|23.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|19.6|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|17.6|21.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|21.1|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|19.3|22.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|21.3|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|18.2|24.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|21.5|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|19.8|23.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|21.8|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|20.1|23.5||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|22.6|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|18.5|26.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|26.4|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|23.8|29.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|26.8|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|25.7|27.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Black|29.2|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|27.7|30.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|4.5|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|2.6|6.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|7.0|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|6.0|8.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|7.9|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|7.2|8.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|8.5|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|7.7|9.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|8.6|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|5.4|11.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|8.7|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|7.0|10.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|9.6|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|8.7|10.5||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|9.7|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.6|12.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|10.3|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|9.6|11.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|10.8|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|5.3|16.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|10.9|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|9.8|12.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|11.4|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|8.9|13.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|11.4|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||11.3|11.5||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|12.2|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|11.2|13.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|12.3|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|10.8|13.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|12.5|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.5|13.5||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|12.5|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|12.0|13.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|12.7|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|11.4|14.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|13.3|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|12.7|13.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|14.0|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|12.0|16.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|14.2|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|11.1|17.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|14.7|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|12.1|17.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|14.9|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|12.5|17.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|20.1|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|17.4|22.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|22.3|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|17.8|26.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Hispanic|22.5|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|18.7|26.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|4.8|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|1.7|7.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|8.5|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|4.8|12.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|9.6|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|6.4|12.8||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|9.6|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.6|13.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|9.7|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|6.2|13.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|10.3|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|7.5|13.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|10.8|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|7.9|13.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|11.6|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|7.9|15.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|12.1|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|6.5|17.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|12.8|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|8.7|16.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|12.9|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|8.1|17.7||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|13.2|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.5|14.9||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|13.7|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||13.4|14.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|14.3|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.2|18.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|14.7|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|10.1|19.3||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|15.2|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.3|21.1||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|15.3|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|13.0|17.6||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|15.6|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|10.2|21.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|16.9|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|13.6|20.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Multiracial|29.7|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|20.0|39.4||
Social and Economic Factors|Percent Unemployed|2012|Both|Other|6.2|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|1.4|11.0||
Social and Economic Factors|Percent Unemployed|2012|Both|Other|13.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander|12.4|15.2||
Social and Economic Factors|Percent Unemployed|2012|Both|Other|15.2|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander; FIPS code 06073 was used to isolate data for San Diego County, CA.|7.3|23.1||
Social and Economic Factors|Percent Unemployed|2012|Both|White|3.6|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|2.7|4.5||
Social and Economic Factors|Percent Unemployed|2012|Both|White|5.2|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.4|6.0||
Social and Economic Factors|Percent Unemployed|2012|Both|White|5.4|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|4.6|6.2||
Social and Economic Factors|Percent Unemployed|2012|Both|White|5.6|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.6|6.6||
Social and Economic Factors|Percent Unemployed|2012|Both|White|5.8|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|5.0|6.6||
Social and Economic Factors|Percent Unemployed|2012|Both|White|5.8|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|5.1|6.5||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.1|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|5.1|7.1||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.4|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.9|6.9||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.4|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|5.8|7.0||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.5|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|5.9|7.1||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.6|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|5.4|7.8||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.7|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|5.5|7.9||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.7|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|5.7|7.7||
Social and Economic Factors|Percent Unemployed|2012|Both|White|6.8|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|6.4|7.2||
Social and Economic Factors|Percent Unemployed|2012|Both|White|7.6|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||7.5|7.7||
Social and Economic Factors|Percent Unemployed|2012|Both|White|7.7|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.0|8.4||
Social and Economic Factors|Percent Unemployed|2012|Both|White|8.0|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|6.8|9.2||
Social and Economic Factors|Percent Unemployed|2012|Both|White|8.2|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|6.8|9.6||
Social and Economic Factors|Percent Unemployed|2012|Both|White|8.2|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|7.6|8.8||
Social and Economic Factors|Percent Unemployed|2012|Both|White|8.4|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|6.8|10.0||
Social and Economic Factors|Percent Unemployed|2012|Both|White|10.4|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|9.8|11.0||
Social and Economic Factors|Percent Unemployed|2012|Both|White|10.4|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|9.5|11.3||
Social and Economic Factors|Percent Unemployed|2012|Both|White|10.6|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|8.9|12.3||
Social and Economic Factors|Percent Unemployed|2012|Both|White|11.7|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.9|12.5||
Social and Economic Factors|Percent Unemployed|2012|Female|All|5.6|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 5363000 was used to isolate data for Seattle, WA.|4.5|6.7||
Social and Economic Factors|Percent Unemployed|2012|Female|All|6.5|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.5|7.5||
Social and Economic Factors|Percent Unemployed|2012|Female|All|7.8|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2507000 was used to isolate data for Boston, MA.|6.8|8.8||
Social and Economic Factors|Percent Unemployed|2012|Female|All|7.9|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|7.1|8.7||
Social and Economic Factors|Percent Unemployed|2012|Female|All|7.9|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.1|8.7||
Social and Economic Factors|Percent Unemployed|2012|Female|All|8.3|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2938000 was used to isolate data for Kansas City, MO.|7.1|9.5||
Social and Economic Factors|Percent Unemployed|2012|Female|All|8.5|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.1|9.9||
Social and Economic Factors|Percent Unemployed|2012|Female|All|8.5|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 06073 was used to isolate data for San Diego County, CA.|7.9|9.1||
Social and Economic Factors|Percent Unemployed|2012|Female|All|8.5|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years|8.4|8.6||
Social and Economic Factors|Percent Unemployed|2012|Female|All|8.6|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.7|9.5||
Social and Economic Factors|Percent Unemployed|2012|Female|All|9.2|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48113 was used to isolate data for Dallas county, TX.|8.5|9.9||
Social and Economic Factors|Percent Unemployed|2012|Female|All|9.9|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0668000 was used to isolate data for San Jose, CA.|8.7|11.1||
Social and Economic Factors|Percent Unemployed|2012|Female|All|10.0|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3651000 was used to isolate data for New York City, NY.|9.6|10.4||
Social and Economic Factors|Percent Unemployed|2012|Female|All|10.1|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 1150000 was used to isolate data for Washington, DC.|8.8|11.4||
Social and Economic Factors|Percent Unemployed|2012|Female|All|10.3|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4835000 was used to isolate data for Houston, TX.|9.3|11.3||
Social and Economic Factors|Percent Unemployed|2012|Female|All|10.7|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.1|12.3||
Social and Economic Factors|Percent Unemployed|2012|Female|All|11.7|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|10.8|12.6||
Social and Economic Factors|Percent Unemployed|2012|Female|All|12.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|11.5|12.5||
Social and Economic Factors|Percent Unemployed|2012|Female|All|12.6|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0653000 was used to isolate data for Oakland, CA.|10.8|14.4||
Social and Economic Factors|Percent Unemployed|2012|Female|All|13.6|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.5|14.7||
Social and Economic Factors|Percent Unemployed|2012|Female|All|14.0|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2404000 was used to isolate data for Baltimore, MD.|12.5|15.5||
Social and Economic Factors|Percent Unemployed|2012|Female|All|15.3|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3916000 was used to isolate data for Cleveland, OH.|13.3|17.3||
Social and Economic Factors|Percent Unemployed|2012|Female|All|22.7|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2622000 was used to isolate data for Detroit, MI.|21.0|24.4||
Social and Economic Factors|Percent Unemployed|2012|Male|All|5.4|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 5363000 was used to isolate data for Seattle, WA.|4.5|6.3||
Social and Economic Factors|Percent Unemployed|2012|Male|All|6.3|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|5.6|7.0||
Social and Economic Factors|Percent Unemployed|2012|Male|All|6.4|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0820000 was used to isolate data for Denver, CO.|5.4|7.4||
Social and Economic Factors|Percent Unemployed|2012|Male|All|6.9|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48113 was used to isolate data for Dallas county, TX.|6.4|7.4||
Social and Economic Factors|Percent Unemployed|2012|Male|All|7.3|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.1|8.5||
Social and Economic Factors|Percent Unemployed|2012|Male|All|8.0|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.9|9.1||
Social and Economic Factors|Percent Unemployed|2012|Male|All|8.1|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4835000 was used to isolate data for Houston, TX.|7.3|8.9||
Social and Economic Factors|Percent Unemployed|2012|Male|All|8.2|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.2|9.2||
Social and Economic Factors|Percent Unemployed|2012|Male|All|8.7|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0668000 was used to isolate data for San Jose, CA.|7.8|9.6||
Social and Economic Factors|Percent Unemployed|2012|Male|All|8.9|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years|8.8|9.0||
Social and Economic Factors|Percent Unemployed|2012|Male|All|9.3|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.6|10.0||
Social and Economic Factors|Percent Unemployed|2012|Male|All|9.3|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 06073 was used to isolate data for San Diego County, CA.|8.8|9.8||
Social and Economic Factors|Percent Unemployed|2012|Male|All|9.9|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2507000 was used to isolate data for Boston, MA.|8.7|11.1||
Social and Economic Factors|Percent Unemployed|2012|Male|All|9.9|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 1150000 was used to isolate data for Washington, DC.|8.6|11.2||
Social and Economic Factors|Percent Unemployed|2012|Male|All|10.1|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2938000 was used to isolate data for Kansas City, MO.|8.8|11.4||
Social and Economic Factors|Percent Unemployed|2012|Male|All|10.1|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3651000 was used to isolate data for New York City, NY.|9.8|10.4||
Social and Economic Factors|Percent Unemployed|2012|Male|All|10.9|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.4|11.4||
Social and Economic Factors|Percent Unemployed|2012|Male|All|11.0|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.4|12.6||
Social and Economic Factors|Percent Unemployed|2012|Male|All|12.1|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.5|12.7||
Social and Economic Factors|Percent Unemployed|2012|Male|All|12.1|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0653000 was used to isolate data for Oakland, CA.|10.6|13.6||
Social and Economic Factors|Percent Unemployed|2012|Male|All|12.2|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|11.4|13.0||
Social and Economic Factors|Percent Unemployed|2012|Male|All|16.0|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2404000 was used to isolate data for Baltimore, MD.|14.4|17.6||
Social and Economic Factors|Percent Unemployed|2012|Male|All|16.6|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|15.4|17.8||
Social and Economic Factors|Percent Unemployed|2012|Male|All|20.7|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3916000 was used to isolate data for Cleveland, OH.|18.7|22.7||
Social and Economic Factors|Percent Unemployed|2012|Male|All|30.0|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2622000 was used to isolate data for Detroit, MI.|27.9|32.1||
Social and Economic Factors|Percent Unemployed|2013|Both|All|5.9|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|5.2|6.6||
Social and Economic Factors|Percent Unemployed|2013|Both|All|5.9|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.3|6.5||
Social and Economic Factors|Percent Unemployed|2013|Both|All|6.8|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|6.3|7.3||
Social and Economic Factors|Percent Unemployed|2013|Both|All|7.2|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|6.3|8.1||
Social and Economic Factors|Percent Unemployed|2013|Both|All|7.6|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|7.1|8.1||
Social and Economic Factors|Percent Unemployed|2013|Both|All|7.8|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.3|8.3||
Social and Economic Factors|Percent Unemployed|2013|Both|All|7.9|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|7.4|8.4||
Social and Economic Factors|Percent Unemployed|2013|Both|All|8.0|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|7.1|8.9||
Social and Economic Factors|Percent Unemployed|2013|Both|All|8.1|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|7.3|8.9||
Social and Economic Factors|Percent Unemployed|2013|Both|All|8.4|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||8.3|8.5||
Social and Economic Factors|Percent Unemployed|2013|Both|All|9.0|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|8.6|9.4||
Social and Economic Factors|Percent Unemployed|2013|Both|All|9.4|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|8.7|10.1||
Social and Economic Factors|Percent Unemployed|2013|Both|All|9.8|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|9.5|10.1||
Social and Economic Factors|Percent Unemployed|2013|Both|All|9.9|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|9.4|10.4||
Social and Economic Factors|Percent Unemployed|2013|Both|All|10.4|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|9.6|11.2||
Social and Economic Factors|Percent Unemployed|2013|Both|All|10.7|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.3|11.1||
Social and Economic Factors|Percent Unemployed|2013|Both|All|11.1|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|10.2|12.0||
Social and Economic Factors|Percent Unemployed|2013|Both|All|11.2|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.6|11.8||
Social and Economic Factors|Percent Unemployed|2013|Both|All|11.5|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|10.4|12.6||
Social and Economic Factors|Percent Unemployed|2013|Both|All|12.7|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|12.2|13.2||
Social and Economic Factors|Percent Unemployed|2013|Both|All|13.8|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|13.0|14.6||
Social and Economic Factors|Percent Unemployed|2013|Both|All|18.1|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|16.7|19.5||
Social and Economic Factors|Percent Unemployed|2013|Both|All|25.3|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|23.7|26.9||
Social and Economic Factors|Percent Unemployed|2013|Both|American Indian/Alaska Native|12.7|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|7.6|17.8||
Social and Economic Factors|Percent Unemployed|2013|Both|American Indian/Alaska Native|14.1|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|7.5|20.7||
Social and Economic Factors|Percent Unemployed|2013|Both|American Indian/Alaska Native|14.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||14.2|15.4||
Social and Economic Factors|Percent Unemployed|2013|Both|American Indian/Alaska Native|15.0|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|9.0|21.0||
Social and Economic Factors|Percent Unemployed|2013|Both|American Indian/Alaska Native|15.6|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|7.0|24.2||
Social and Economic Factors|Percent Unemployed|2013|Both|American Indian/Alaska Native|19.5|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|13.4|25.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|2.2|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|0.5|3.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|4.3|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|2.7|5.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|4.8|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|3.5|6.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|5.5|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|2.1|8.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|5.7|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|2.8|8.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|6.2|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|4.1|8.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|6.5|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.6|8.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|6.9|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|5.0|8.8||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.0|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2938000 was used to isolate data for Kansas City, MO.|1.8|12.2||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.0|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|5.7|8.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.3|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.6|9.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.4|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0820000 was used to isolate data for Denver, CO.|4.2|10.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.4|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.2|8.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.5|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.5|8.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.8|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|7.0|8.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|7.8|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 3651000 was used to isolate data for New York City, NY.|7.2|8.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|8.3|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.3|12.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|8.6|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|7.5|9.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|9.0|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|6.6|11.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|11.3|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|8.5|14.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Asian/PI|11.4|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|8.6|14.2||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|12.5|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|10.5|14.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|12.6|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|7.7|17.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|12.7|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|11.5|13.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|12.7|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|10.0|15.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|13.7|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|11.5|15.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|13.9|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|11.4|16.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|14.1|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|12.0|16.2||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|14.2|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|13.5|14.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|14.5|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|13.0|16.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|14.8|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|13.5|16.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|15.2|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||15.0|15.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|16.6|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|12.5|20.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|16.7|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|15.2|18.2||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|17.4|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|12.8|22.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|17.7|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|15.9|19.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|18.9|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|17.1|20.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|19.0|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|14.6|23.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|20.0|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|18.2|21.8||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|20.9|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|18.1|23.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|24.6|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|23.4|25.8||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|24.9|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|20.9|28.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|25.6|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|23.3|27.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Black|26.9|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|25.1|28.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|3.6|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|1.6|5.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|6.2|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|5.5|6.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|6.7|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|6.0|7.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|6.8|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|5.4|8.2||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|6.9|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|2.6|11.2||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|7.3|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|6.1|8.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|7.3|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|4.0|10.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|8.5|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.7|9.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|9.1|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|6.5|11.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|9.5|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.4|11.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|10.0|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|9.3|10.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|10.0|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||9.9|10.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|10.7|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|7.4|14.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|10.9|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|8.8|13.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|11.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.4|11.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|11.4|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|9.1|13.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|11.5|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.5|12.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|11.9|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.0|12.8||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|12.1|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|10.6|13.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|12.2|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.5|12.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|12.9|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|11.2|14.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|15.3|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|12.3|18.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|17.5|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|14.7|20.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Hispanic|23.2|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|18.7|27.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|4.8|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|2.0|7.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|7.1|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|3.9|10.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|7.9|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.1|11.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|9.8|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|5.9|13.7||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|10.6|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.7|14.5||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|11.0|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|8.6|13.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|11.1|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|7.4|14.8||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|11.3|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|7.2|15.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|11.4|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|6.5|16.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|12.0|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|6.0|18.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|12.3|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||12.0|12.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|12.9|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|9.9|15.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|13.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.9|15.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|13.1|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|8.2|18.0||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|13.7|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|8.8|18.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|15.0|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.7|19.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|16.5|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|11.6|21.4||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|17.0|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|10.9|23.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|17.7|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|11.1|24.3||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|21.6|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|14.6|28.6||
Social and Economic Factors|Percent Unemployed|2013|Both|Multiracial|29.5|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|17.9|41.1||
Social and Economic Factors|Percent Unemployed|2013|Both|Other|12.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander|11.7|13.9||
Social and Economic Factors|Percent Unemployed|2013|Both|Other|14.3|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.8|21.8||
Social and Economic Factors|Percent Unemployed|2013|Both|Other|16.1|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|9.3|22.9||
Social and Economic Factors|Percent Unemployed|2013|Both|White|3.2|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|2.5|3.9||
Social and Economic Factors|Percent Unemployed|2013|Both|White|4.3|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|3.6|5.0||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.0|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.5|5.5||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.0|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|4.1|5.9||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.0|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.4|5.6||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.1|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|4.2|6.0||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.2|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|4.5|5.9||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.4|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|4.6|6.2||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.4|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|4.1|6.7||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.5|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|4.8|6.2||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.8|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|4.6|7.0||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.8|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.3|6.3||
Social and Economic Factors|Percent Unemployed|2013|Both|White|5.9|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|5.3|6.5||
Social and Economic Factors|Percent Unemployed|2013|Both|White|6.2|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|5.9|6.5||
Social and Economic Factors|Percent Unemployed|2013|Both|White|6.5|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|5.5|7.5||
Social and Economic Factors|Percent Unemployed|2013|Both|White|6.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||6.7|6.9||
Social and Economic Factors|Percent Unemployed|2013|Both|White|7.0|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|5.8|8.2||
Social and Economic Factors|Percent Unemployed|2013|Both|White|7.6|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|6.2|9.0||
Social and Economic Factors|Percent Unemployed|2013|Both|White|7.9|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|7.3|8.5||
Social and Economic Factors|Percent Unemployed|2013|Both|White|8.0|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.3|8.7||
Social and Economic Factors|Percent Unemployed|2013|Both|White|8.3|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|7.2|9.4||
Social and Economic Factors|Percent Unemployed|2013|Both|White|9.4|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.8|10.0||
Social and Economic Factors|Percent Unemployed|2013|Both|White|9.6|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.9|10.3||
Social and Economic Factors|Percent Unemployed|2013|Both|White|10.3|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|8.5|12.1||
Social and Economic Factors|Percent Unemployed|2013|Both|White|14.6|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|11.2|18.0||
Social and Economic Factors|Percent Unemployed|2013|Female|All|4.5|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 5363000 was used to isolate data for Seattle, WA.|3.7|5.3||
Social and Economic Factors|Percent Unemployed|2013|Female|All|5.9|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0820000 was used to isolate data for Denver, CO.|4.9|6.9||
Social and Economic Factors|Percent Unemployed|2013|Female|All|6.2|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|5.5|6.9||
Social and Economic Factors|Percent Unemployed|2013|Female|All|6.8|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2507000 was used to isolate data for Boston, MA.|5.6|8.0||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.1|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.2|8.0||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.5|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|6.3|8.7||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.5|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.9|8.1||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.7|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48113 was used to isolate data for Dallas county, TX.|7.0|8.4||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.7|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2938000 was used to isolate data for Kansas City, MO.|6.3|9.1||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.7|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years|7.6|7.8||
Social and Economic Factors|Percent Unemployed|2013|Female|All|7.8|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.0|8.6||
Social and Economic Factors|Percent Unemployed|2013|Female|All|8.0|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4835000 was used to isolate data for Houston, TX.|7.3|8.7||
Social and Economic Factors|Percent Unemployed|2013|Female|All|9.0|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.1|9.9||
Social and Economic Factors|Percent Unemployed|2013|Female|All|9.0|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0668000 was used to isolate data for San Jose, CA.|8.0|10.0||
Social and Economic Factors|Percent Unemployed|2013|Female|All|9.0|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 1150000 was used to isolate data for Washington, DC.|7.8|10.2||
Social and Economic Factors|Percent Unemployed|2013|Female|All|9.1|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3651000 was used to isolate data for New York City, NY.|8.7|9.5||
Social and Economic Factors|Percent Unemployed|2013|Female|All|9.3|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.5|10.1||
Social and Economic Factors|Percent Unemployed|2013|Female|All|9.5|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2404000 was used to isolate data for Baltimore, MD.|8.2|10.8||
Social and Economic Factors|Percent Unemployed|2013|Female|All|10.2|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|9.4|11.0||
Social and Economic Factors|Percent Unemployed|2013|Female|All|10.7|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.2|11.2||
Social and Economic Factors|Percent Unemployed|2013|Female|All|11.1|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0643000 was used to isolate data for Long Beach, CA.|9.6|12.6||
Social and Economic Factors|Percent Unemployed|2013|Female|All|11.9|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|11.2|12.6||
Social and Economic Factors|Percent Unemployed|2013|Female|All|12.4|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0653000 was used to isolate data for Oakland, CA.|10.7|14.1||
Social and Economic Factors|Percent Unemployed|2013|Female|All|12.4|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|11.3|13.5||
Social and Economic Factors|Percent Unemployed|2013|Female|All|14.6|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3916000 was used to isolate data for Cleveland, OH.|12.6|16.6||
Social and Economic Factors|Percent Unemployed|2013|Female|All|22.2|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2622000 was used to isolate data for Detroit, MI.|20.2|24.2||
Social and Economic Factors|Percent Unemployed|2013|Male|All|5.2|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0820000 was used to isolate data for Denver, CO.|4.2|6.2||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.2|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48113 was used to isolate data for Dallas county, TX.|5.7|6.7||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.4|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|5.7|7.1||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.4|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2938000 was used to isolate data for Kansas City, MO.|5.3|7.5||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.5|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|5.4|7.6||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.5|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 5363000 was used to isolate data for Seattle, WA.|5.5|7.5||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.8|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4835000 was used to isolate data for Houston, TX.|6.2|7.4||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.8|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.0|7.6||
Social and Economic Factors|Percent Unemployed|2013|Male|All|6.8|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0667000 was used to isolate data for San Francisco, CA.|6.0|7.6||
Social and Economic Factors|Percent Unemployed|2013|Male|All|8.0|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years|7.9|8.1||
Social and Economic Factors|Percent Unemployed|2013|Male|All|8.4|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.5|9.3||
Social and Economic Factors|Percent Unemployed|2013|Male|All|8.7|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0668000 was used to isolate data for San Jose, CA.|7.7|9.7||
Social and Economic Factors|Percent Unemployed|2013|Male|All|9.2|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 06073 was used to isolate data for San Diego County, CA.|8.6|9.8||
Social and Economic Factors|Percent Unemployed|2013|Male|All|9.6|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|9.1|10.1||
Social and Economic Factors|Percent Unemployed|2013|Male|All|9.6|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3651000 was used to isolate data for New York City, NY.|9.2|10.0||
Social and Economic Factors|Percent Unemployed|2013|Male|All|10.2|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0643000 was used to isolate data for Long Beach, CA.|8.9|11.5||
Social and Economic Factors|Percent Unemployed|2013|Male|All|10.8|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0653000 was used to isolate data for Oakland, CA.|9.3|12.3||
Social and Economic Factors|Percent Unemployed|2013|Male|All|11.4|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.7|12.1||
Social and Economic Factors|Percent Unemployed|2013|Male|All|11.4|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 1150000 was used to isolate data for Washington, DC.|10.2|12.6||
Social and Economic Factors|Percent Unemployed|2013|Male|All|11.5|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|10.6|12.4||
Social and Economic Factors|Percent Unemployed|2013|Male|All|11.8|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2404000 was used to isolate data for Baltimore, MD.|10.4|13.2||
Social and Economic Factors|Percent Unemployed|2013|Male|All|13.7|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.4|15.0||
Social and Economic Factors|Percent Unemployed|2013|Male|All|19.2|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3916000 was used to isolate data for Cleveland, OH.|17.0|21.4||
Social and Economic Factors|Percent Unemployed|2013|Male|All|26.8|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2622000 was used to isolate data for Detroit, MI.|24.5|29.1||
Social and Economic Factors|Percent Unemployed|2014|Both|All|4.5|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|4.0|5.0||
Social and Economic Factors|Percent Unemployed|2014|Both|All|4.7|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|4.1|5.3||
Social and Economic Factors|Percent Unemployed|2014|Both|All|6.2|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|5.7|6.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|6.2|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.7|6.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|6.6|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|6.0|7.2||
Social and Economic Factors|Percent Unemployed|2014|Both|All|6.7|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|6.3|7.1||
Social and Economic Factors|Percent Unemployed|2014|Both|All|6.7|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|5.8|7.6||
Social and Economic Factors|Percent Unemployed|2014|Both|All|6.7|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|6.2|7.2||
Social and Economic Factors|Percent Unemployed|2014|Both|All|7.1|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.5|7.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|7.2|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||7.1|7.3||
Social and Economic Factors|Percent Unemployed|2014|Both|All|7.5|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|7.2|7.8||
Social and Economic Factors|Percent Unemployed|2014|Both|All|7.6|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|6.7|8.5||
Social and Economic Factors|Percent Unemployed|2014|Both|All|8.1|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|7.7|8.5||
Social and Economic Factors|Percent Unemployed|2014|Both|All|8.2|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.7|8.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|8.3|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|7.5|9.1||
Social and Economic Factors|Percent Unemployed|2014|Both|All|8.3|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|8.1|8.5||
Social and Economic Factors|Percent Unemployed|2014|Both|All|8.9|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|8.1|9.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|9.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.7|9.3||
Social and Economic Factors|Percent Unemployed|2014|Both|All|9.4|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.9|9.9||
Social and Economic Factors|Percent Unemployed|2014|Both|All|10.9|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.5|11.3||
Social and Economic Factors|Percent Unemployed|2014|Both|All|11.8|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|10.9|12.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|12.8|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.0|13.6||
Social and Economic Factors|Percent Unemployed|2014|Both|All|18.4|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|17.1|19.7||
Social and Economic Factors|Percent Unemployed|2014|Both|All|21.6|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|20.4|22.8||
Social and Economic Factors|Percent Unemployed|2014|Both|American Indian/Alaska Native|7.8|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|3.2|12.4||
Social and Economic Factors|Percent Unemployed|2014|Both|American Indian/Alaska Native|8.1|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|4.3|11.9||
Social and Economic Factors|Percent Unemployed|2014|Both|American Indian/Alaska Native|12.8|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|4.7|20.9||
Social and Economic Factors|Percent Unemployed|2014|Both|American Indian/Alaska Native|12.8|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|6.3|19.3||
Social and Economic Factors|Percent Unemployed|2014|Both|American Indian/Alaska Native|13.3|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|8.1|18.5||
Social and Economic Factors|Percent Unemployed|2014|Both|American Indian/Alaska Native|13.3|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||12.8|13.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|3.0|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 1150000 was used to isolate data for Washington, DC.|1.0|5.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|3.6|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2404000 was used to isolate data for Baltimore, MD.|1.5|5.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|3.6|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4865000 was used to isolate data for San Antonio, TX.|1.6|5.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|4.5|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|1.6|7.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|4.5|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 5363000 was used to isolate data for Seattle, WA.|3.0|6.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|4.6|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4835000 was used to isolate data for Houston, TX.|3.2|6.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|4.8|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 06073 was used to isolate data for San Diego County, CA.|4.0|5.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|5.0|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 48113 was used to isolate data for Dallas county, TX.|3.6|6.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|5.4|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|3.4|7.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|5.6|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone|5.5|5.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|6.0|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0643000 was used to isolate data for Long Beach, CA.|3.7|8.3||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|6.2|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|2.6|9.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|6.3|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0668000 was used to isolate data for San Jose, CA.|5.4|7.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|6.5|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|5.8|7.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|6.6|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.6|7.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|6.9|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|4.9|8.9||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|7.0|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|4.2|9.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|7.2|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.8|8.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|8.4|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 0653000 was used to isolate data for Oakland, CA.|5.8|11.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|10.0|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2507000 was used to isolate data for Boston, MA.|7.0|13.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Asian/PI|19.9|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Asian alone; FIPS code 2622000 was used to isolate data for Detroit, MI.|12.0|27.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|7.4|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|3.3|11.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|7.6|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|5.0|10.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|8.4|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|6.1|10.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|10.9|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|9.8|12.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|11.0|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|9.6|12.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|11.2|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|6.9|15.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|12.0|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|11.4|12.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|12.2|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|10.2|14.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|12.5|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|10.1|14.9||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|13.3|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|11.0|15.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|14.0|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|11.3|16.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|14.4|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|11.7|17.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|14.8|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|13.4|16.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|15.3|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|13.1|17.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|15.4|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|13.8|17.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|16.1|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|14.7|17.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|17.0|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|15.4|18.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|17.9|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|13.0|22.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|18.3|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|16.7|19.9||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|18.7|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|14.1|23.3||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|18.9|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|15.5|22.3||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|20.6|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|19.5|21.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|23.6|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|22.1|25.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Black|26.3|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|24.5|28.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|5.1|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|3.7|6.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|5.3|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|3.0|7.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|5.8|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|3.9|7.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|5.9|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|4.5|7.3||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|6.1|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|5.5|6.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|6.4|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|3.8|9.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|6.5|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|5.8|7.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|6.7|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|5.4|8.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|6.8|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|6.3|7.3||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|8.1|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|7.3|8.9||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|8.3|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|7.0|9.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|8.4|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||8.3|8.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|8.8|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|5.2|12.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|9.1|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|8.1|10.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|9.3|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.3|10.3||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|9.3|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.8|9.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|9.4|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|7.8|11.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|9.7|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|9.2|10.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|9.8|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|9.1|10.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|10.1|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|9.3|10.9||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|12.6|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|10.1|15.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|13.4|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|8.7|18.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|13.7|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|9.8|17.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Hispanic|16.5|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|13.8|19.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|6.4|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|3.2|9.6||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|8.6|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|5.1|12.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|9.3|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|6.1|12.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|9.3|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|5.9|12.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|9.5|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|6.6|12.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|9.8|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|7.8|11.8||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|10.0|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|8.5|11.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|10.4|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2507000 was used to isolate data for Boston, MA.|6.7|14.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|11.1|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||10.8|11.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|11.5|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|7.6|15.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|11.8|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|7.9|15.7||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|12.0|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|9.5|14.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|12.3|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|10.2|14.4||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|13.0|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|8.1|17.9||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|13.2|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|7.2|19.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|13.5|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|8.8|18.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|14.2|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|9.9|18.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|14.5|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|8.5|20.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|14.9|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|10.3|19.5||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|18.5|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.9|24.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|20.1|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|9.1|31.1||
Social and Economic Factors|Percent Unemployed|2014|Both|Multiracial|21.9|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|11.8|32.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Other|4.3|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander; FIPS code 06073 was used to isolate data for San Diego County, CA.|0.6|8.0||
Social and Economic Factors|Percent Unemployed|2014|Both|Other|10.6|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|5.0|16.2||
Social and Economic Factors|Percent Unemployed|2014|Both|Other|10.7|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Native Hawaiian or other Pacific Islander|9.7|11.7||
Social and Economic Factors|Percent Unemployed|2014|Both|White|2.7|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 1150000 was used to isolate data for Washington, DC.|2.2|3.2||
Social and Economic Factors|Percent Unemployed|2014|Both|White|3.2|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4835000 was used to isolate data for Houston, TX.|2.7|3.7||
Social and Economic Factors|Percent Unemployed|2014|Both|White|3.6|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 5363000 was used to isolate data for Seattle, WA.|3.0|4.2||
Social and Economic Factors|Percent Unemployed|2014|Both|White|3.8|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0820000 was used to isolate data for Denver, CO.|3.3|4.3||
Social and Economic Factors|Percent Unemployed|2014|Both|White|4.0|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2743000 was used to isolate data for Minneapolis, MN.|3.2|4.8||
Social and Economic Factors|Percent Unemployed|2014|Both|White|4.2|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0653000 was used to isolate data for Oakland, CA.|3.2|5.2||
Social and Economic Factors|Percent Unemployed|2014|Both|White|4.4|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0667000 was used to isolate data for San Francisco, CA.|3.8|5.0||
Social and Economic Factors|Percent Unemployed|2014|Both|White|4.7|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2938000 was used to isolate data for Kansas City, MO.|4.0|5.4||
Social and Economic Factors|Percent Unemployed|2014|Both|White|4.8|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48113 was used to isolate data for Dallas county, TX.|4.3|5.3||
Social and Economic Factors|Percent Unemployed|2014|Both|White|4.8|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4865000 was used to isolate data for San Antonio, TX.|4.1|5.5||
Social and Economic Factors|Percent Unemployed|2014|Both|White|5.1|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0668000 was used to isolate data for San Jose, CA.|4.3|5.9||
Social and Economic Factors|Percent Unemployed|2014|Both|White|5.3|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.7|5.9||
Social and Economic Factors|Percent Unemployed|2014|Both|White|5.5|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3651000 was used to isolate data for New York City, NY.|5.2|5.8||
Social and Economic Factors|Percent Unemployed|2014|Both|White|5.7|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2404000 was used to isolate data for Baltimore, MD.|4.5|6.9||
Social and Economic Factors|Percent Unemployed|2014|Both|White|5.8|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|5.2|6.4||
Social and Economic Factors|Percent Unemployed|2014|Both|White|5.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.|||5.7|5.9||
Social and Economic Factors|Percent Unemployed|2014|Both|White|6.1|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 06073 was used to isolate data for San Diego County, CA.|5.6|6.6||
Social and Economic Factors|Percent Unemployed|2014|Both|White|6.6|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0455000 was used to isolate data for Phoenix, AZ.|5.9|7.3||
Social and Economic Factors|Percent Unemployed|2014|Both|White|7.3|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 4260000 was used to isolate data for Philadelphia, PA.|6.4|8.2||
Social and Economic Factors|Percent Unemployed|2014|Both|White|7.6|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0644000 was used to isolate data for Los Angeles, CA.|7.1|8.1||
Social and Economic Factors|Percent Unemployed|2014|Both|White|8.1|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 0643000 was used to isolate data for Long Beach, CA.|6.3|9.9||
Social and Economic Factors|Percent Unemployed|2014|Both|White|8.3|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|7.6|9.0||
Social and Economic Factors|Percent Unemployed|2014|Both|White|8.8|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 3916000 was used to isolate data for Cleveland, OH.|7.0|10.6||
Social and Economic Factors|Percent Unemployed|2014|Both|White|11.9|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||FIPS code 2622000 was used to isolate data for Detroit, MI.|8.9|14.9||
Social and Economic Factors|Percent Unemployed|2014|Female|All|3.6|Seattle, WA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 5363000 was used to isolate data for Seattle, WA.|2.9|4.3||
Social and Economic Factors|Percent Unemployed|2014|Female|All|4.4|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0820000 was used to isolate data for Denver, CO.|3.5|5.3||
Social and Economic Factors|Percent Unemployed|2014|Female|All|4.8|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|3.7|5.9||
Social and Economic Factors|Percent Unemployed|2014|Female|All|5.3|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0667000 was used to isolate data for San Francisco, CA.|4.6|6.0||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.0|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|5.3|6.7||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.4|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4865000 was used to isolate data for San Antonio, TX.|5.6|7.2||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.6|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48113 was used to isolate data for Dallas county, TX.|6.1|7.1||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.6|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4835000 was used to isolate data for Houston, TX.|5.8|7.4||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.6|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years|6.5|6.7||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.8|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0653000 was used to isolate data for Oakland, CA.|5.6|8.0||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.8|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0668000 was used to isolate data for San Jose, CA.|6.0|7.6||
Social and Economic Factors|Percent Unemployed|2014|Female|All|6.9|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.4|7.4||
Social and Economic Factors|Percent Unemployed|2014|Female|All|7.0|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2507000 was used to isolate data for Boston, MA.|6.0|8.0||
Social and Economic Factors|Percent Unemployed|2014|Female|All|7.0|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2938000 was used to isolate data for Kansas City, MO.|5.8|8.2||
Social and Economic Factors|Percent Unemployed|2014|Female|All|7.4|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|6.5|8.3||
Social and Economic Factors|Percent Unemployed|2014|Female|All|7.7|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3651000 was used to isolate data for New York City, NY.|7.4|8.0||
Social and Economic Factors|Percent Unemployed|2014|Female|All|8.1|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 1150000 was used to isolate data for Washington, DC.|6.9|9.3||
Social and Economic Factors|Percent Unemployed|2014|Female|All|8.2|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|7.5|8.9||
Social and Economic Factors|Percent Unemployed|2014|Female|All|8.8|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.1|9.5||
Social and Economic Factors|Percent Unemployed|2014|Female|All|9.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|8.6|9.4||
Social and Economic Factors|Percent Unemployed|2014|Female|All|10.1|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2404000 was used to isolate data for Baltimore, MD.|8.7|11.5||
Social and Economic Factors|Percent Unemployed|2014|Female|All|10.2|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0643000 was used to isolate data for Long Beach, CA.|8.3|12.1||
Social and Economic Factors|Percent Unemployed|2014|Female|All|10.6|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|10.0|11.2||
Social and Economic Factors|Percent Unemployed|2014|Female|All|15.6|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3916000 was used to isolate data for Cleveland, OH.|13.7|17.5||
Social and Economic Factors|Percent Unemployed|2014|Female|All|18.6|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2622000 was used to isolate data for Detroit, MI.|17.1|20.1||
Social and Economic Factors|Percent Unemployed|2014|Male|All|4.4|Denver, CO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0820000 was used to isolate data for Denver, CO.|3.7|5.1||
Social and Economic Factors|Percent Unemployed|2014|Male|All|5.3|Fort Worth (Tarrant County), TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48439 was used to isolate data for Fort Worth (Tarrant County), TX.|4.7|5.9||
Social and Economic Factors|Percent Unemployed|2014|Male|All|5.4|Houston, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4835000 was used to isolate data for Houston, TX.|4.8|6.0||
Social and Economic Factors|Percent Unemployed|2014|Male|All|5.7|Dallas, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 48113 was used to isolate data for Dallas county, TX.|5.3|6.1||
Social and Economic Factors|Percent Unemployed|2014|Male|All|5.8|Kansas City, MO|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2938000 was used to isolate data for Kansas City, MO.|4.8|6.8||
Social and Economic Factors|Percent Unemployed|2014|Male|All|5.8|San Jose, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0668000 was used to isolate data for San Jose, CA.|5.1|6.5||
Social and Economic Factors|Percent Unemployed|2014|Male|All|6.3|San Antonio, TX|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4865000 was used to isolate data for San Antonio, TX.|5.4|7.2||
Social and Economic Factors|Percent Unemployed|2014|Male|All|6.7|San Francisco, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0667000 was used to isolate data for San Francisco, CA.|5.8|7.6||
Social and Economic Factors|Percent Unemployed|2014|Male|All|6.8|U.S. Total, U.S. Total|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years|6.7|6.9||
Social and Economic Factors|Percent Unemployed|2014|Male|All|7.0|Minneapolis, MN|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2743000 was used to isolate data for Minneapolis, MN.|5.7|8.3||
Social and Economic Factors|Percent Unemployed|2014|Male|All|7.1|San Diego County, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 06073 was used to isolate data for San Diego County, CA.|6.7|7.5||
Social and Economic Factors|Percent Unemployed|2014|Male|All|7.5|Oakland (Alameda County), CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0653000 was used to isolate data for Oakland, CA.|6.2|8.8||
Social and Economic Factors|Percent Unemployed|2014|Male|All|7.7|Phoenix, AZ|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0455000 was used to isolate data for Phoenix, AZ.|7.1|8.3||
Social and Economic Factors|Percent Unemployed|2014|Male|All|7.8|Miami (Miami-Dade County), FL|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 12086 was used to isolate data for Miami (Miami-Dade County), FL.|7.1|8.5||
Social and Economic Factors|Percent Unemployed|2014|Male|All|7.9|Boston, MA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2507000 was used to isolate data for Boston, MA.|6.8|9.0||
Social and Economic Factors|Percent Unemployed|2014|Male|All|8.0|Los Angeles, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0644000 was used to isolate data for Los Angeles, CA.|7.5|8.5||
Social and Economic Factors|Percent Unemployed|2014|Male|All|8.0|New York City, NY|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3651000 was used to isolate data for New York City, NY.|7.6|8.4||
Social and Economic Factors|Percent Unemployed|2014|Male|All|8.4|Long Beach, CA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 0643000 was used to isolate data for Long Beach, CA.|6.9|9.9||
Social and Economic Factors|Percent Unemployed|2014|Male|All|8.7|Las Vegas (Clark County), NV|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 32003 was used to isolate data for Las Vegas (Clark County), NV.|8.0|9.4||
Social and Economic Factors|Percent Unemployed|2014|Male|All|9.3|Washington, DC|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 1150000 was used to isolate data for Washington, DC.|8.2|10.4||
Social and Economic Factors|Percent Unemployed|2014|Male|All|9.7|Chicago, Il|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; Place=Chicago was used to isolate American Community Survey data for Chicago, IL on American Factfinder.|9.1|10.3||
Social and Economic Factors|Percent Unemployed|2014|Male|All|13.3|Baltimore, MD|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2404000 was used to isolate data for Baltimore, MD.|11.8|14.8||
Social and Economic Factors|Percent Unemployed|2014|Male|All|14.0|Philadelphia, PA|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 4260000 was used to isolate data for Philadelphia, PA.|12.8|15.2||
Social and Economic Factors|Percent Unemployed|2014|Male|All|18.7|Cleveland, OH|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 3916000 was used to isolate data for Cleveland, OH.|16.5|20.9||
Social and Economic Factors|Percent Unemployed|2014|Male|All|23.0|Detroit, MI|Percent of unemployment among population 16 and over using US Census Bureau, American Community Survey 1-year estimates, or something similar.|US Census Bureau, American Community Survey 1-year estimates; Table ID S2301 - Employment Status.||Population 20 to 64 years; FIPS code 2622000 was used to isolate data for Detroit, MI.|21.0|25.0||